February 2016

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING

FEBRUARY 2016 VOL -6 NO - 2

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.2 FEBRUARY 2016

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.2 FEBRUARY 2016

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From Editor's Desk Dear Researcher, Greetings!

Research article in this issue discusses about motivational factor analysis.

Let us review research around the world this month.

Neuroscientists find that Cognitive Skills Peak at different ages a new research from neuroscientists and Massachusetts General Hospital reveals that different parts of the brain work best at different ages.

Scientists have long known that our ability to think quickly and recall information also known as fluid intelligence, peaks around age 20 and then begins a slow decline. However, more recent findings, including a new study from neuroscientists at MIT and Massachusetts General Hospital (MGH), suggest that the real picture is much more complex. The researchers also included a vocabulary test, which serves as a measure of what is known as crystallized intelligence the accumulation of facts and knowledge. Gathering more data from their websites and have added new cognitive tasks designed to evaluate social and emotional intelligence, language skills and executive function. They are also working on making their data public so that other researchers can access it and perform other types of studies and analyses.

It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.2 FEBRUARY 2016

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia.

Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.2 FEBRUARY 2016

Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.

Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.

Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.

Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India).

Dr. Ing. RostislavChotČborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,ýeskázemČdČlskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

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Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ.

Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar)01332-000, São Paulo

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(SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.

Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India.

Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt 603 203, India.

Mr. Md. Musfique Anwar B.Sc(Engg.)

Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.

Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom.

Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India.

Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India.

Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India

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Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.

MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India.

Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukiü Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

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Contents An Automated Guide Map Application For Blind People Using Android Device Mythily.V & Sathish.R ……………….……………………………….…………………………………….[329] Augmentation of Face Recognition and Retrieval (AFRR) Under Various Illuminations Using Block Truncation Coding Dr.S.Prasath….................…………………….…………………………………….[332]

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.6 NO.2 FEBRUARY 2016

An Automated Guide Map Application for Blind People Using Android Device Mythily.V Second year M.E Department of Computer Science and Engineering, Surya Engineering College, Erode Tamil Nadu, India.

Email: mythilycse12@gmail.com Sathish.R Assistant Professor Department of Computer Science and engineering, Surya Engineering College, Erode, Tamil Nadu, India.

Email: belovedsathish@gmail.com Abstract—The objective of this paper is to guide blind people with voice navigated GPS using an Android Phone.

This application is an innovative and cost effective guide system for blind people. For blind and visually impaired people is quite impossible to be autonomous in the contemporary world, in which we are completely surrounded by information, but only visual information.

While crossing a road the visually impaired person can easily identify the vehicles by using sensor. By clicking an icon the user get the current location. In this paper the authors describe a new system based on Android technology and designed for trying to solve this situation of impossibility of information that afflicts blind people.

Keywords- Android, GPS, Text to Speech, RF, GIS. 1. INTRODUCTION Blindness are visual impairment is a condition that affects many persons around the world. This condition leads to the loss of the precious sense of vision to such a degree that makes the concerned person handicapped due to the need for guidance or assistance and in some cases special treatment. Guidance by other humans, with good vision, or specially trained dogs is an obvious solution to help blind persons navigate their way around both in the house as well as outside the house. However, dependence on other humans is highly demanding and constraining in many ways. Trained dogs are very helpful however they have limitations that include inability to interpret what the blind persons really wants and identifying the objects. This is in addition to the continuous care cost for the dog.

Some technological solutions have been introduced recently to help blind persons to navigate independently.

Many of those solutions rely on Global Positioning System (GPS) technology to identify the position and orientation of the blind person. While such systems are suitable for outdoor navigation, due to the need for line of sight access to satellites, they still need addition components to improve on the resolution and proximity detection to prevent collision of the blind persons with other objects and hence subject his/her life to danger.

The use of robot-dog is another technological solution proposed by a number of researchers. The robot-dog is an attempt to replace the real dog. It also depends on GPS and

incorporates objects avoidances technologies. These solutions are useful, however they can only be used outdoor and miss interpretation of the blind person requests as well as accuracy issues may have serious consequences on the well being of the user.

The navigation system uses TTS (Text-to-Speech) for blindness in order to provide a navigation service through voice. Also, it uses Google Map API to apply map information [1].

The blind gives the input about the place he has to reach using microphones and the voice recognition system recognizes it .The input is then analyzed by the microcontroller which generates the bus numbers corresponding to the location provided by the blind [2].

The Electronic Path Guidance will help them to move in an unfamiliar place independently and with the same simplicity as they are in familiar surroundings. For this we are using the RF (Radio Frequency) communication [3]. When the blind person wears this ultrasonic waist-belt at stomach or at head, which consist of an ultrasonic distance sensor, Ultrasonic distance sensor, which is capable of detecting obstacles in its path of a blind person, senses the obstacles.

This information is passed to the microcontroller which then alerts the user through voice circuit in case of any obstacles in that particular direction, which helps the user to avoid obstacles in its way. The controlling device of the whole system is a Microcontroller [4].

The currently widespread solution, also known as an accessible pedestrian signal (APS), uses a special sound/speech output to notify blind people about the status of a pedestrian signal at an intersection. However, this solution requires installation of expensive equipment at the intersections, limiting its applicability [5].

This paper presents a system that will enable blind or visually impaired person to navigate independently in the outdoors. The system integrates wireless communication technologies, path planning, sensors and other technologies to build a compact portable navigation system.

2. EXISTING SYSTEM Finding an ROUTE or branch near to us is possible through GIS. A geographic information system is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographically referenced

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data. The locator to find the services you require - simply enter your postcode, town or city and click on 'Search' to see all ROUTEs in your area. GIS is the merging of cartography, statistical analysis, and database technology. In a general sense, the term describes any information system that integrates, stores, edits, analyzes, shares, and displays geographic information for informing decision making.

GIS a more complex mapping technology that is connected to a particular database. Because it’s generic, it is a broader term than the GPS in its technical sense. Thus, GIS is a computer program or application that is utilized to view and handle data about geographic locations and spatial correlations among others.

It simply gives the user a framework to obtain information.

3. PROPOSED SYSTEM Identifies location & navigates you to the nearest Place or ROUTE. Get turn-by-turn directions to the nearest Place or ROUTE. The Global Positioning System (GPS) is a space-based satellite navigation system that provides location and time information in all weather, anywhere on or near the Earth, where there is an unobstructed line of sight to four or more GPS satellites. It is maintained by the United States government and is freely accessible by anyone with a GPS receiver.

The GPS (Global Position System) is a network that locates certain places here on earth whereas the GIS (Geographic Information System) is a computer program that process data linked to certain places or locations.

3.1 ADVANTAGES OF PROPOSED SYSTEM • Space-based satellite navigation system.

• Provides location and time information in all weather, anywhere on or near the Earth.

• If we enter into wrong root then automatic wrong root indication will give by voice. If we are at near then automatic nearest place indication will give by voice.

• To identify the current location using GPS connection in the way of voice guided system.

Fig. 1 Image of GIS Identifies location & navigates you to the nearest Place or ROUTE. Get turn-by-turn directions to the nearest Place or ROUTE. The Global Positioning System (GPS) is a space-based satellite navigation system that provides location and time information in all weather.

3.2 SYSTEM ARCHITECTURE

This system includes four modules which are used for the propose of navigation of blind without the help of other persons.

Fig. 2 Image of GPS

¾ ¾ ¾ ¾ ¾ ¾

2.1 DISADVANTAGES Merging of cartography, statistical analysis, and database technology.

Complex mapping technology.

GIS is a computer program or application.

No wrong root indication.

No nearest indication.

While using GPS, it doesn’t identify the current location through voice.

Fig. 3 Process Flow

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The system gets the sources as input from the user as command then it gets the second input which is the destination. Then the route map of given source and destination will be displayed. When the person starts walking the direction of walking will b given in the form of command (spoken out). For finding the route map the route tracker map is used. Then for the speaking command the command guided system is used.

From the given source and destination the map is obtained by the use of GPS. The person starts walking from his/her source to the destination. Then each and everyone turn is indicate as the spoken command. Even the wrong way indication is made by the use GPS. This GPS provides the location and time information in all weather, anywhere on Earth.

4. CONCLUSION This paper presented the architecture and implementation of a system that assists a blind person to navigate in the outdoor. The system can be considered as a semi-autonomous device. It provides full autonomy for global navigation, but relies on the skills of user for the navigation.

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Ivanchenko, V., Coughlan, J., and Shen, H. Detecting and locating crosswalks using a camera phone. In Computer Vision and Pattern Recognition Workshops ( 2014).

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Shioyama, T., Wu, H., Nakamura, N., and Kitawaki, S. Measurement of the length of pedestrian crossings and detection of traffic lights from image data.

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Uddin, M. and Shioyama, T. Detection of Pedestrian Crossing using Bipolarity and Projective Invariant. In IAPR Conference on Machine Vision Applications (2012).

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Lienhart, R. and Maydt, J. An Extended Set of HaarLike Features for Rapid Object Detection. In IEEE International Conference on Image Processing (2013).

Pleis, J.R. and Lethbridge-Çejku, M.Summary health statistics for U.S.adults: National Health Interview Survey, 2010. National Center for Health Statistics, 2012.

Giudice, N.A. and G.E. Legge. Blind Navigation and the Role ofTechnology. In A. Helal, M. Mokhtari and B. Aldulrazak, ed., The Engineering Handbook of Smart Technology for Aging, Disability and Independence. John Wiley & Sons, Hoboken, New Jersey(2013).

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Gallo, O., Manduchi, R., and Rafii, A. Robust Curb and Ramp Detection for Safe Parking using the Canesta TOF camera. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2012).

Grundman, T., Eidenberger, R., Zoellner, R.D., Zhixing, X., Ruehl, S.,Zoellner, J.M., Dillmann, R., Kuehnle, J., and Verl, A. Integration of 6D Object Localization and Obstacle Detection for Collision Free Robotic Manipulation. In IEEE/SICE International Symposium on System Integration (2013).

Bostelman, R.V., Hong, T.H., and Madhavan, R. Towards AGV Safety and Navigation Advancement Obstacle Detection using a TOF Range Camera. In International Conference on Advanced Robotics (2010).

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Augmentation of Face Recognition and Retrieval (AFRR) Under Various Illuminations Using Block Truncation Coding Dr.S.Prasath Assistant Professor, Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India.

Email: softprasaths@gmail.com Abstract— A simple and efficient Block Truncation Coding (BTC) method to compress two dimensional color shape without loss of data as well as quality of the picture.

BTC is one of the lossy image compression algorithms but in this research work has proposed on extended version of image compression using BTC. The given color RGB value is converted into gray scale image. The features are extracted and constructed in matrix form. Then the matrix form is divided into block. Finally compressed block table is generated. The proposed algorithm is tested and implemented on various parameters. The experiments and shows that proposed method provides better recognition rate when compared with the existing methods then experiments are carried out using MATLAB. Keywords- BTC, RGB, SV, MV, GSV, CB, CR, MSE, PSNR.

1. INTRODUCTION Today Information Technology plays a vital role in every field of human life. Digital image Processing is rapidly growing area for last few decades and involved in various fields of research as well in human life also. Present, digital imaging is popularly technology and used in many industrial solutions concerning. In this process industry several on-line measurement systems are based on the digital imaging. As a result of this rapid development, the amount of image data has increased rapidly and leads to a complexity in data storage and management. Consequently the size of different kinds of image databases in the industry has increased significantly. Therefore managing of these databases for further processing becomes a tedious one. Hence, lot of techniques and methods has evolved to overcome those issues.

2. RELATED WORKS Aditya Kumar et.al [1] presented the improved BTC algorithm, namely Enhanced Block Truncation Coding was presented in this research study. Several gray scale images are used to appraise the coding efficiency and performance of this proposed algorithm with existing image compression algorithm. In the EBTC algorithm, here bit plane is encoded to improve the compression ratio and also improve the image quality. Simulation results show that bit rate and PSNR of this proposed EBTC are better than BTC as well as the ABTC in terms of subject visual quality and significantly improves the compression ratio of BTC. Gaganpreet Kaur et al. [2] analyzed lossy and lossless image compression techniques. They studied and discussed all the techniques. Finally, concluded lossy compression

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techniques provided high compression ratio than lossless compression scheme. Lossy compression is used for more compression ratio and Lossless compression is used when the original image and reconstructed image are to be identical.

Prabhakar et al. [3] proposed the efficient medical image compression as a combined approach of SPIHT and LempelZiv-Welch coding. This combined approach was applied for both color and gray scale images. They concluded that compressed image with good compression ratio and good PSNR value. The proposed method was comparatively better than the existing algorithms regarding PSNR and Compression ratio.

Jun Wang et. al [4] presented an improved motion adaptive codec. The IMAC contains two approaches to improve the motion adaptive codec overdrive. The first was an advanced hybrid image codec for the efficient reduction of the image data stored in frame memory. The second was an advanced motion adaptive selector to reduce the overdrive error.

Lucas Hui [5] proposed a method of designing a minimum mean square error quantizer for the block truncation coding algorithm for image compression is presented. Based on this MMSE quantizer, an adaptive block truncation coding algorithm is designed. This method tries to optimize the quantization output levels based on the locality of the image pixel blocks and significantly improved reconstructed image quality was achieved at 2 b/pixel. The ABTC method is very efficient as compared to the standard BTC and the AMBTC methods.

Lema et. al [6] presented the Color image data compression using absolute moment block truncation coding scheme was implemented. This compression technique reduces the computational complexity and achieves the optimum minimum mean square error and PSNR. It was an improvised version of BTC, obtained by preserving absolute moments.

AMBTC is an encoding technique that preserves the spatial details of digital images while achieving a reasonable compression ratio.

Meftah et. al [7] proposed a method called the Improved Adaptive Block Truncation Coding based on Adaptive Block Truncation Coding. The feature of inter-pixel redundancy is exploited to reduce the bit-rate further by retaining the quality of the reconstructed images.

Pravin et al. [8] presented comparative of various image compression techniques to assess the progress made in the field of image compression effects on different images for different applications. They reviewed about the image compression, need of compression, its principles, and types of

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compression and different algorithms used for image compression.

Delphi et. al [9] proposed to improve the transmission rate and storage space the compression of image is very necessary. For compression of image many tools and methods are applied. They are developed block truncation coding (BTC) was easy to implement. The bit rate obtained by BTC is 2 bpp but the quality of reconstructed image was very low.

Kaspar Raj et. al [10] proposed three and two stage still gray scale image compressor based on BTC. They have employed a combination of four techniques to reduce the bit rate. They are quad tree segmentation, bit plane omission, bit plane coding using 32 visual patterns and interpolative bit plane coding. The experimental results show that the proposed schemes achieve an average bit rate of 0.46bits per pixel for standard gray scale images with an average PSNR value of 30.25, which is better than the results from the exiting similar methods based on BTC. Somasundaram et. al [11] proposed an extended version of Absolute Moment Block Truncation Coding to compress images. Generally the elements of a bitplane used in the variants of Block Truncation Coding are of size 1 bit. But it has been extended to two bits in the proposed method.

Somasundram et. al [12] proposed method the feature of inter-pixel correlation was exploited to further reduce the requirement of bits to store a block. The proposed method gives very good performance in terms of bit-rate and PSNR values when compared to the conventional BTC. The interpixel redundancy feature is incorporated in the conventional BTC method and this has lead to further reduction in the bit rate and improvement in the PSNR of the reconstructed images. The EBTC method was tried with different threshold values in categorizing the low and high detail blocks. As the threshold value increases, the PSNR was improved and the bitrate was reduced. The technique was tried with different images and yielded better results with less computational effort.

Vimala et. al [13] proposed a method called the Improved Adaptive Block Truncation Coding based on Adaptive Block Truncation Coding. The feature of inter-pixel redundancy is exploited to reduce the bit-rate further by retaining the quality of the reconstructed images. The proposed method outperforms the existing BTC based methods both in terms of bit-rate and PSNR values.

3. METHODOLOGY In the existing system an image compression and decompression using Block truncation code algorithm has been developed for a grayscale image. The below sample example clearly shows the importance of compression. An image has 1024Ă—1024 pixel/24 bit, without compression, would require 3 MB of storage and 7 minutes for transmission, utilizing a high speed, 64 Kbits/sec. If the image is compressed at a 10:1 compression ratio, the storage requirement is reduced to 300 KB and the transmission time drop to less than 6 sec.

3.1.1 Block Truncation Coding (BTC) Block Truncation Coding (BTC) is a well-known compression scheme proposed in 1979 for the grayscale images. It was also called the moment-preserving block

truncation because it preserves the first and second moments of each image block. The amount of image data grows day by day. Large storage and bandwidth are needed to store and transmit the images, which is quite costly. Hence methods to compress the image data are essentially now-a-days. Block Truncation Coding is a simple technique which involves less computational complexity. BTC is a recent technique used for compression of monochrome image data. It is one-bit adaptive moment-preserving quantizer that preserves certain statistical moments of small blocks of the input image in the quantized output. The original algorithm of BTC preserves the standard mean and the standard deviation. The statistical overheads mean and the Standard deviation are to be coded as part of the block. Various methods have been proposed during last twenty years for image compression such BTC and Absolute Moment Block Truncation Coding (AMBTC). AMBTC preserves the higher mean and lower mean of the blocks and use this quantity to quantize output. AMBTC provides enhanced image quality than image compression using BTC. 3.1.2 Standard BTC

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3.1.3 Absolute Moment Block Truncation Coding (AMBTC) In this technique image compression is done using absolute moment block truncation coding. It is an improved version of BTC, preserves absolute moments rather than standard moments, here also a digitized image is divided into blocks of n x n pixels. Each block is quantized in such a way that each resulting block has the same sample mean and the same sample first absolute central moment of each original block. A. Image Compression Using AMBTC.AMBTC has been extensively used in signal compression because of its simple computation and better mean squared error (MSE) performance. It has the advantages of preserving single pixel and edges having low computational complexity. The original algorithm preserves the block mean and the block standard deviation. Other choices of the moments result either in less MSE or less computational complexity. In AMBTC algorithm similar to BTC there are four separate steps while coding a single block of size n x n. They are quad tree segmentation, bit plane omission, bit plane coding using 32 visual patterns and interpolative bit plane coding. In this work, used compressed the image using AMBTC algorithm.

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¾

Original image is segmented into blocks of size 4x4 or 8x8 for processing. ¾ Find the mean of each block and also find levels from the mean which has higher range and lower range. ¾ Based on these calculated mean value for each block, bit plane is calculated. ¾ Image block is reconstructed by replacing the ‘1’s with higher mean and ‘0’s with lower mean. At the encoder side an image is divided into nonoverlapping blocks.

The size of each non overlapping block may be (4 x 4) or (8 x 8), etc. and then it is calculate the average gray level of the block (4x4) as (for easy understanding we made the compression for 4 x 4 block).

3.2 Multilevel Block Truncation Coding (MLBTC) Block Truncation Coding has been used. It has been implemented on four levels are, 3.2.1 BTC Level 1 A face image is taken from the database and the average intensity value of each color plane of the image is calculated. The color space considered in this algorithm is the RGB color space. So the average intensity value of each of the RGB plane of a face image is calculated. The further discussion is done using the Red plane of an image. The same has to be carried out for the Blue and Green color space. After obtaining the average value intensity value of the Red color plane of the face image, each pixel is compared with the mean value and the image is divided into two regions: Upper Red and Lower Red. The average intensity values of these regions is calculated and stored in the feature vector as UR and LR. Thus, after repeating this procedure for the Blue and the Green color space our feature vector has six elements: Upper Red, Lower Red, Upper Green, Lower Green, Upper Blue and Lower Blue.

3.2.2 BTC Level 2 At level two the values Upper Red and Lower Red are extracted from the feature vector of BTC level 1 and using these values, the Red plane of face image is now divided into four regions. These are Upper-Upper Red, Upper-Lower Red, Lower-Upper Red and Lower-Lower Red. The average intensity values in these four regions is calculated and stored in the feature vectors. The above process is reiterated for the Blue and Green color spaces of the face image. Thus the feature vector at this level has 12 elements, 4 elements for each plane.

3.2.3 BTC Level 3 and BTC Level 4 Using the procedures described in the Levels 1 & 2, the face images are further divided into more regions in each of the color space. These regions are depicted in figure1. The average intensity value at these regions are calculated and stored in the feature vector. The feature vector has 24 elements at BTC-level 3 and 48 elements at BTC-level 4. The feature vectors obtained in BTC-levels 1,2,3,4 are used for comparison with the database images set.

3.3 BTC-INTERMEDIATE-4 & BTC-INTERMEDIATE-9 To calculate the feature vector in this algorithm, Block Truncation Coding has been used. It has been implemented on four levels which are explained below.

3.3.1 BTC-Intermediate-4 Level 1 In BTC-Intermediate-4, the given image is partitioned into four non overlapping equal parts. Each part of the image is then considered individually. After this , BTC is applied on each part of the given image .For each part the average intensity value of each of the RGB plane is calculated independently and which helps in dividing the RGB plane into upper and lower clusters respectively. The 6 elements (UR, LR, UG, LG, UB and LB) for an image. Further we calculate the mean value of all the four feature vectors of the four parts of the given image to obtain the feature vector of the entire image. This is the basic implementation of BTC-Intermediate4 method applied on BTC based face recognition level 1 and hence we call it as BTC-Intermediate-4 Level 1.

3.3.2 BTC-Intermediate-4 Level 2 In level 2 implementation of BTC based face recognition of BTC-Intermediate-4 get 12 elements for each feature vector of the four parts of the given image. Thus to obtain the feature vector of the image, the mean value of all the four feature vectors is calculated.

3.3.3 BTC-Intermediate-4 Level 3 and BTCIntermediate-4 Level 4 Similarly in level 3 and level 4 implementation of BTC based face recognition of BTC-Intermediate-4 get 24 elements and 48 elements respectively. Thus the mean value is calculated at both the levels in order to obtain the feature vector of the entire image.

3.3.4 BTC-Intermediate-9 The implementation of BTC-Intermediate-9 is same as that of BTC-Intermediate-9, but in this method the image is divided into nine non overlapping equal parts. Similarly to obtain the feature vector of the given image the mean value of all the feature vectors of the nine parts is calculated. The implementation of BTC-Intermediate-9 is also done up to 4 levels.

3.4 PROPOSED AUGMENTATION OF FACE RECOGNITION AND RETERIVAL (AFRR) The main objective of this proposed system is to improve the quality of the image and reduce the size. This research proposes an image compression technique using a Improved Block Truncation Coding (IBTC) to compress the image. The following figure shows that block diagram of proposed system.

Initially, all the images are preprocessed. In color based approaches certain region of interest is cropped from the images in order to avoid processing unnecessary detail present in the face. During training stage features are extracted from every image and are stored separately in the database. While testing a search image features are extracted for that image and are matched against all the images in the database. The dissimilarity measure used to match feature is chi-square. The

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performance of the proposed approach is studied using color features separately and combined.

Input

Preprocessing

Where, NF is the normalized dissimilarity measure Ϯ for every image in the input database image dž ǁ(Ι, Τ) is the Ϯ Ϯ dissimilarity measure, max dž ǁ (Ι, Τ ) and min dž ǁ (Ι, Τ ) are the maximum and minimum distance measures among the input database images respectively.

R

G

Threshold

Threshold

4. SIMILARITY AND PERFORMANCE MEASURES 4.1 False Acceptance Rate (FAR) False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) are standard performance evaluation parameters of face recognition system. The False acceptance rate (FAR) is the measure of the likelihood that the biometric security system will incorrectly accept an access attempt by an unauthorized user. A system’s FAR typically is stated as the ratio of the number of false acceptances divided by the number of identification attempts.

FAR = (False Claims Accepted/Total Claims) X 100 ---> (4) 4.2 Genuine Acceptance Rate (GAR) The Genuine Acceptance Rate (GAR) is evaluated by subtracting the FAR values from 100.

GAR=100-FAR (in percentage) ---> (5)

B

ThresholĚ

Target Image

Feature Database

Threshold Chi-Square Matching & Retrieval

5. ALGORITHM The process of the face images is take place in two phases and defined as algorithm I and algorithm II. In algorithm I results in the feature extraction and in the retrieval of face. In algorithm II the face matching is take place and the GAR and FAR has estimated and are as follows.

Resultant Image

Fig.3 Proposed Face recognition process ALGORITHM I 3.4.1 Feature Extraction The feature vector at each BTC level for the query image and database set is extracted. Here three binary bitmaps values will be computed. If a pixel in each component (R, G and B) for threshold.

The feature vectors obtained in each level of BTC are used to compare with the database images. The comparison of similarity measures is done by Chi-square statistic defined as equation (2).

Step 1 : Select an input image I of size M x N (Even row and column) Step 2 : Convert gray scale values into matrix format.

Step 3 : Apply sorting method for an array by using step 2.

Step 4 : Find out the middle gray scale values of lower range and upper range.

---> (2) Where,

// To Calculate threshold value //

Step 5 : Find out the average value of middle gray scale

is the ith feature value of the gallery set

is the ith feature value of the search one. The image and dissimilarity among the images for color features using weighted Chi-square defined in equation (3).

values and take whole number in sorted array and also known as threshold value.

Step6 : Convert binary matrix by using threshold value.

Step 7 : Apply the weighted Chi-square to target image and input image by using equation (3).

---> (3) and are the jth feature In the above formula of ith region in the input database image and search image respectively. wi is the weight of ith region. Then normalize dissimilarity measures for color features individually for the images in the training set .

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Step 8: Repeat step 2 to step 6 for all images in the database.

Step9: Stop

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ALGORITHM II

rate is the measure of the likelihood that the biometric security system will incorrectly accept an access attempt by an unauthorized user. A system FAR typically is stated as the ratio of the number of false acceptances divided by the number of identification attempts. The average FAR and GAR of all queries in respective face databases are considered for performance ranking of BTC levels. For optimal performance the FAR values must be less and accordingly the GAR values must be high for each successive levels of BTC. Fig.4 Sample Images from Face Database

Input : Face Image Output : Feature set //Generation of Face Image Feature set // Step 1: Select an input image I of size M x N Step 2: Divide the input image I into non-overlapping blocks with size wĂ—w .

Step 3: Color Features in the form of vector in the form of histogram are computed for input images and are stored in a database.

Step 4: For every search image determine Color features vector for the image.

Step 5: The certain region in the face have more importance, compute the dissimilarity among the images for color features using weighted Chi-square by using equation (3).

Step 6: Compute normalize dissimilarity measures for color features individually for the images in the training set by applying equation (4).

Step 7: Then calculate normalized measures for every image in the input database images.

Step 8: The input database image which give up least dissimilarity measure with the target image is considered as the recognized one.

Step 9: Repeat step 5 to step 6 for all images in the database.

Step 10: Stop

Table.1 GAR values at different BTC levels MLBTC Intermediate-4, MLBTC-Intermediate-9 and AFRR Recognition in % Name

ALGORITHM III Input : Target Image Output : Face Image Matching Step 1: Step 2: Step 3:

Step 4: Step 5:

// Face Image Matching// Select the target image L of size M x N and divide into m x n block.

Repeat step 5 to step 6 as in the algorithm II. Compute the Chi-square distance between the target image and the image set for matching using the equation 3 and 4.

Compute the GAR and FAR using the equation 4 and equation 5.

Stop

BTC Interme diate-4

BTC Interme diate-9

MLBTC

AFRR

BTCLevel 1

95.66

96.94

97.04

98.2

BTCLevel 2

97.40

97.55

98.01

98.56

97.48

97.65

98.23

99.02

97.62

97.84

98.39

99.15

BTCLevel 3 BTCLevel 4

Table.2 FAR values at different BTC levels MLBTC Intermediate-4 , MLBTC-Intermediate-9 and AFRR Recognition in %

6. EXPERIMENTATION AND RESULTS The proposed AFRR feature extraction is experimented with the images collected from the standard database. Therefore all the images are rotated in such a way that a line connecting iris centers lies in a horizontal line and the following experimental setup, parameter settings are used.

This database is created by Dr Libor consisting of 1000 images (each with 180 pixels by 200 pixels), corresponding to 100 persons in 10 poses each, including both males and females.

All the images are captured against a dark background, little variation in illumination. The subjects are next to fixed distance from the camera are taken with sequence of images.

The ten poses of Face database are shown in figure 4.

False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR) are standard performance evaluation parameters of face recognition system. The false acceptance

Name

BTCLevel 1 BTCLevel 2 BTCLevel 3 BTCLevel 4

BTC Interm ediate -4

BTC Intermedi ate-9

MLBTC

AFRR

0.043

0.030

0.0296

0.018

0.026

0.024

0.0199

0.014

0.025

0.023

0.0177

0.017

0.023

0.021

0.0161

0.008

From the below Table.1 gives the experimental results the AFRR produces higher recognition accuracy of 99.15% for faces partially occluded with spectacles. This shows that AFRR is more suited than the other tested BTC in recognizing

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faces partially occluded with spectacles. The total claims, false rejections and GAR values of the four BTC levels based face recognition methods tested on face database are shown. False rejections refer to number of relevant images which were rejected. Thus BTC-level 4 based face recognition technique gives us the least false number of rejections and highest GAR value indicating best performance. From the below Table.2 gives the experimental results the AFRR produces higher recognition accuracy of for faces partially occluded with spectacles. This shows that AFRR is more suited than the other tested BTC in recognizing faces partially occluded with spectacles. The total claims, false rejections and GAR values of the four BTC levels based face recognition methods tested on face database are shown. False rejections refer to number of relevant images which were rejected. Thus BTC-level 4 based face recognition technique gives us the least false number of rejections and highest GAR value indicating best performance.

From the below figure 4 shows the pictorial representation of the performance evaluated. By analyzing the obtained results the JPG produced the best results.

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

Fig 5. Performance Evaluation

7. CONCLUSION Digital Image Processing is on emerging field in the area of computer science and other application area. Digital Image Processing compression plays a vital on role of all field.

A new image compression scheme based on BTC and maximum-minimum pixels value has been proposed. The face recognition of color features capture the face and geometrical features describe the shape details of the facial components.

But when they are applied alone face recognition may not produce good results. This paper proposes to mixture of two features to enhance the accuracy of face recognition. The performance of color feature extraction methods and geometrical methods are investigated independently and combined together when compared existing methods.

[1]

8. REFERENCES Aditya Kumar,Pardeep Singh, "Enhanced Block Truncation Coding for Gray Scale Image" Int. J. Comp. Tech. Appl., Vol 2 (3), 525-529,2012ISSN:2229-6093.

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@IJITCE Publication