HAND GESTURE RECOGNITION SYSTEM by IRJET Journal

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072

HAND GESTURE RECOGNITION SYSTEM

Jean Jacob1, Abhimanyu VM2, Ashin Girish3, Ashly Shaji4, Bhagyalakshmi Biju5

1Assistant professor, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of Engineering, Kadayirupppu, Ernakulam, Kerala, India

2,3,4,5UG student, Dept. of Computer Science and Engineering, Sree Narayana Gurukulam College Of Engineering, Kadayirupppu, Ernakulam, Kerala, India

Abstract- Hand gesture recognition systems have gained significant attention in recent years due to their potential applications in human-computer interaction, virtual reality, robotics, and various other fields. This paper presents a comprehensive overview of a HGRS designed to accurately interpret and respond to HHM. The proposed system leverages advanced computer vision techniques, machine learning algorithms, and deep neural networks to achieve robust and realtime hand gesture recognition. The process begins with hand detection using state-of-the-art algorithms, followed by hand tracking to ensure continuous monitoring of gestures. The system then extracts relevant features from the hand movements, considering factors such as hand shape, orientation, and motion dynamics. To enhance the accuracy and adaptability of the system, a machine learning model is trained on a diverse dataset of hand gestures. Transfer learning techniques are employed to fine-tune the model on specific gesture categories, allowing the system to recognize a wide range of gestures with high precision.

I. INTRODUCTION

1.1GENERALBACKGROUND

Hand gesture recognition is a technology that enables computers to interpret and respond to HHM, allowing for intuitive and natural interaction between humans and machines. This field has gained increasing prominence in recent years due to its applications in diverse areas, including human-computer interaction, virtual reality, augmented reality, robotics, and healthcare. The primary goal of hand gesture recognition systems is to accurately interpretthegesturesmadebyusers,translatingtheminto meaningful commands or actions. These systems typically employ a combination of computer vision, image processing, and machine learning techniques. Computer vision algorithms are utilized to detect and track the movement of hands in images or videos, while machine learningmodels,oftenbasedondeepneuralnetworks,are employed to recognize specific gestures based on learned patterns and features. The challenges in hand gesture recognition include variations in hand poses, lighting conditions, and the need for real-time processing. Researchers and developers address these challenges by exploring innovative algorithms, leveraging large datasets

for training, and incorporating advanced neural network architectures. The practical applications of hand gesture recognition are diverse, ranging from gaming and virtual reality experiences to hands-free control in smart environments. As technology continues to advance, hand gesture recognition systems are expected to play a pivotal role in enhancing user interfaces and facilitating more naturalandimmersiveinteractionswithmachines.

1.2OBJECTIVE

The main goal of a HGRS is to create technology that lets computersunderstandandrespondtoHHMaccuratelyand quickly. This involves making sure the system can recognize a variety of hand gestures, work in real-time, handle different environments, and be easy for users to interact with. The system should also be adaptable to differentdevicesandcapableoflearningnewgesturesover time. The practical applications include improving how peopleinteractwithcomputers,virtualrealityexperiences, gaming,andotherareas.

II. LITERATURE SURVEY

The purpose of a literature review is to, as the name suggests, “review” the literature surrounding a certain topic area. The word “literature” means “sources of information” or “research.” The literature will inform us abouttheresearchthathasalreadybeenconductedonour chosensubject.

Paper [1]. "Real-Time Hand Gesture Recognition Using ConvolutionalNeuralNetworks"by C.Cao,Y.Wu,Z.Xu:In thedynamiclandscapeofhuman-computerinteraction,the paper by C. Cao, Y. Wu, and Z. Xu, titled "Real-Time Hand Gesture Recognition Using Convolutional Neural Networks," stands as a pivotal contribution. The authors address the critical need for instantaneous responsiveness in computing systems, particularly in scenarios requiring realtimeinteraction.Thekeyinnovationliesinthestrategic utilization of Convolutional Neural Networks (CNNs), a class of deep learning models renowned for their proficiency in imagerelated tasks. The paper introduces a system that leverages CNNs to accurately and swiftly recognize dynamic hand gestures. CNNs, with their

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072

hierarchical feature extraction capabilities, prove to be well-suited for discerning the intricate spatial patterns inherent in hand movements. The system's architecture enablesadelicatebalancebetweenefficiencyandaccuracy, ensuringthatreal-timeresponsivenessisachievedwithout compromising the precision of gesture recognition. This breakthrough holds implications for a myriad of applications, including virtual reality experiences, interactive gaming, and hands-free control interfaces, wheretheimmediacyofresponseisparamount.

Paper[2]."ASurveyofVision-BasedHandGesture Recognition"byC.Keskin,F.Kirac,Y.E.Kara,andL.Akarun:

In the expansive realm of vision-based hand gesture recognition,thesurveyauthoredbyC.Keskin,F.Kirac,Y.E. Kara, and L. Akarun, titled "A Survey of VisionBased Hand GestureRecognition,"servesasacomprehensiveguideand a panoramic window into the evolution of this multidisciplinary field. The authors meticulously navigate throughtheintricaciesofvariousmethodologies,offeringa detailed exploration of hand detection techniques, feature extraction methods, and classification approaches. This survey not only presents a historical perspective but also synthesizes contemporary research, providing a nuanced understanding of the challenges and advancements in visionbased hand gesture recognition. By acting as a compendiumofknowledge,thepaperaidsresearchersand practitioners in navigating the complex landscape of techniques, enabling them to make informed decisions in theirpursuitofenhancinggesturerecognitionsystems.The interdisciplinary nature of vision-based hand gesture recognitionisunderscored,emphasizingtheamalgamation of computer vision, machine learning, and humancomputerinteractionprinciples.

Paper [3]. "Dynamic Hand Gesture Recognition for HumanComputerInteraction"byP.S.Huang,L.R.Shie,Y.H. Chen: The paper authored by P. S. Huang, L. R. Shie, and Y. H. Chen, titled "Dynamic Hand Gesture Recognition for HumanComputerInteraction,"addressesacrucialaspectof gesturerecognitionoftenoverlookedintraditionalsystems the temporal dynamics of dynamic hand gestures. Recognizing that dynamic gesturesinvolvecontinuous and evolving movements, the authors introduce an innovative approach that employs Hidden Markov Models (HMMs) to capture the temporal dependencies inherent in such gestures.By incorporating HMMs, the system gains the ability to model the evolving nature of dynamic gestures, leading to heightened accuracy and a more sophisticated interpretation of user inputs. This contribution significantly advances the state-of-the-art in humancomputer interaction, allowing systems to not only recognize discrete gestures but also comprehend the fluidity of continuous hand movements. The implications extend to applications requiring natural and nuanced

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interactions, such as sign language interpretation or immersivevirtualrealityexperiences.

Paper [4]. "Hand Gesture Recognition Using Depth and Skeletal Information" by T. Ouyang, X. Wang: Tackling the challenges associated with diverse lighting conditions and the need for robust recognition, T. Ouyang and X. Wang delveintotheintegrationofdepthinformationandskeletal tracking in their paper titled "Hand Gesture Recognition Using Depth and Skeletal Information." Traditional approaches often struggle in challenging lighting environments, leading to compromised performance. This paper recognizes these limitations and proposes a novel system that considers both depth data and skeletal joint information for a more comprehensive understanding of hand movements. The synergy between these two sources ofinformationresultsinamorerobustgesturerecognition system. By incorporating depth information, the system gains additional spatial cues, while skeletal tracking enhances the precision of gesture recognition. The proposed approach addresses challenges faced by conventionalmethodsanddemonstratestheimportanceof considering multidimensional information sources for holistichandgestureanalysis.Thisworkcontributestothe broader conversation on adaptability in real-world scenarios, where environmental conditions may vary significantly.

Paper [5]. "Gesture Recognition Using 3D Deep Features" by X. Li, K. Xu, Y. Zhang:In the realm of three-dimensional hand gesture recognition, X. Li, K. Xu, and Y. Zhang make significantstrideswiththeirpapertitled"Gesture

Recognition Using 3D Deep Features." This research underscores the complexity and nuance of threedimensional hand movements, introducing a system that extracts deep features from depth data. By incorporating 3D convolutional neural networks (CNNs) and leveraging deep learning techniques, the proposed system achieves high accuracy in discerning intricate and nuanced hand gestures. The significance of this work lies in its emphasis on capturing spatial and temporal nuances in threedimensional space. Traditional approaches often struggle with the intricate details of hand gestures, especiallyinapplicationswherefine-grainedrecognitionis crucial. The introduction of deep features enhances the system's accuracy, marking a notable advancement in the landscape of 3D-based gesture recognition methodologies. This work holds implications for applications requiring detailed and precise hand gesture interpretation, such as virtualrealityexperiencesormedicaldiagnostics.

Paper [6]. "Real-Time Hand Gesture Recognition Using Depth

Sensors: A Comprehensive Review" by A. Sharma, B. K. Garg,

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072

C. S. Rai: Sharma, Garg, and Rai present a comprehensive review in their paper titled "Real-Time Hand Gesture Recognition Using Depth Sensors," shedding light on the advancements and challenges in the field. With the increasingpopularityofdepthsensors,thispaperprovides a thorough examination of real-time gesture recognition techniques that leverage depth information. The authors explore various methodologies, including sensor technologies, hand tracking, and recognition algorithms. The paper delves into the nuances of depth-based hand gesture recognition, discussing the advantages and

limitationsofdifferentapproaches.Itaddresseschallenges such as occlusion, environmental factors, and the need for accurate depth data. By providing a detailed overview of the existing literature, the authors contribute to the understanding of the evolving landscape in real-time hand gesture recognition. This work is a valuable resource for researchers and practitioners aiming to navigate the complexities associated with depth sensor-based gesture recognitionsystems.

TABLE 1. COMPARATIVE STUDY OF RECOMMENDATION SYSTEM

Method

Hand Segmentation Techniqueto Hand Gesture Recognition for Natural Human Computer Interaction

AReal-Time

Hand Gesture RecognitionMethod

Hand Gesture Recognition

Usinga Neural Network Shape Fitting Technique

Hand Gesture Recognition

BasedOn FEMDWith A Commodity Depth Camera

III. CONCLUSION

Colour space Segmentation technique Gesture Recognition method

RGB, HSV, CIE-Lab

Anticipatedstatic gestureset, HSL algorithm, HTS algorithm

Edge traversal algorithm

Accuracy Remarks

Best results under complex background

HSV Skin collector method Scalespacefeature detection 0.938 Speed of the system satisfy RT requirements

YCbCrS SGONG network

RGB, HSV

Likelihoodbased classification 0.904SGONG network converges faster and captures feature spaceeffectively

Timeseriescurve Template matching using FEMD

0.906 System is robust to scalechanges

IV. REFERENCES

The report on hand gesture recognition systems provides insights into various methodologies and techniques employed in the dynamic field of human-computer interaction. The evaluated systems showcase diverse approaches, leveraging colour spaces, segmentation techniques,andadvancedrecognitionmethods.Thereport provides a comprehensive overview of the state of the art inhandgesturerecognition,showcasingtheadvancements, challenges, and potential directions for future research. The methods discussed pave the way for continued innovation in natural human-computer interaction, with implications for diverse applications such as gaming, virtualreality,healthcare,andmore.

Archana S. Ghotkar & Gajanan K. Kharate, “Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Computer Interaction”, International Journal of Human Computer Interaction (IJHCI),Volume(3):Issue(1):2012

E. Stergiopoulou, N. Papamarkos, “A New Technique for Hand Gesture Recognition”,IEEE-ICIP,pp.,2006.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 11 Issue: 04 | Apr 2024 www.irjet.net p-ISSN: 2395-0072

Y. Cui and J. Weng, “View-based hand segmentation and hand sequence recognition with complex backgrounds,” in Proceedings of 13th ICPR. Vienna, Austria,Aug.1996,vol. 3,pp.

Atsalakis,A.,Papamarkos,N.,2005a. Color reduction by using a new self-growing and self-organized neural network. In: VVG05: Second International Con- ference onVision,VideoandGraphics,Edinburgh,UK,pp.53–60.

Kjeldsen, R., Kender, J., 1996. Finding skin in colour images. In: IEEE Second International Conference on Automated Face and Gesture Recognition, Kill- ington, VT,USA,pp.184–188.

Kohonen, T., 1990. The self-organizing map ProceedingsofIEEE78(9),1464–1480.

MKohonen,T.,1997.Self-OrganizingMaps,seconded. Springer, Berlin. Licsar, A., Sziranyi, T., 2005. Useradaptive HGRSwith interactive training. Image and Vision Computing 23 (12),1102–1114