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Image reconstruction and on-the-fly minutiae extraction of fingerprints acquired with sweep sensors Giuseppe Parziale Institute of Digital Image Processing Joanneum Research

Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology

Abstract: To abruptly reduce the production costs of fingerprint systems and push up their wide-spreading, some chip-makers introduced on the market new kind of fingerprint sensors. They are characterized by their reduced dimensions and a special procedure to acquire the fingerprint, since the finger has to sweep over the sensing area. During the finger movement, the device generates a certain number of frames representing small portions of the ridge-valley pattern. We present an algorithm for the reconstruction of the fingerprints acquired with these sensors. Moreover, we show that is possible to extract minutiae on-the-fly, without the need to store the whole frame sequence.



Automatic fingerprint recognition systems represent 52% of the entire worldwide biometrics market [3]. To further push up the wide-spreading and reduce the cost of these biometrics systems, chip-makers introduced on the market a new generation of low-cost fingerprint sensors [1]. While the common devices (touching sensors) have a surface with a size comparable to the fingertip (commonly 1 inch Ă— 1 inch), these new sensors (sweep sensors) have a very small acquiring area (reduced of a factor up to 32). This size reduction leads to an easier integration on mobile and handheld devices (PDAs, mobile-phones, laptop, smart-card), a reduction of the power consumption (less transistors have to be supplied) and a reduction of production costs, due to the less used silicon. However, an alternative procedure to acquire the fingerprint is needed. Due to its reduced dimensions, this device is able to acquire only a small portion of the fingertip skin. Thus, to obtain a useful description of the ridge-valley pattern, the finger has to sweep over the sensing area (Fig. 1), while consecutive portions of the finger are stored. The complete fingerprint is then reconstructed, computing the amount of overlap between two successive frames and re-composing the image. Due to the recent introduction of these sensors on the market, no algorithms for the image reconstruction have been published yet. Vendors have their own proprietary solutions and keep

them secret. Here, we present for the first time an algorithm for reconstruction of fingerprint images obtained by sweep sensors. Besides, while the common approach is to first reconstruct the image and, then, extract the feature points (minutiae), we extract the minutiae on-the-fly. This leads to a gain of memory usage, since only two successive frames have to be stored for the image processing and not the entire frame sequence. The reduction of the memory usage is very important, since on handheld and mobile devices (mobile phones, PDAs), the available memory is normally limited.

Figure 1: Sweeping of the finger on the sensor.


Image reconstruction

To estimate the amount of overlap between two consecutive frames, we use a correlation technique. Let S1 , . . . , SN represent the frames, captured at the instants t1 , . . . , tN , respectively. Given a region R in a frame Si , we want to find the displacement [∆x, ∆y] of that region in the successive frame Si+1 that minimizes the following error: ED (u, v) =

X (x,y)∈R

[Si (x, y) − Si+1 (x, y)]2 =


[Si (x, y) − Si (x + ∆x, y + ∆y)]2



This is the standard Sum-of-Squared-Differences (SSD) measure [2]. Once the displacement [∆x, ∆y] is obtained, it is easy to compute the amount of overlap1) between two successive frames. Applying the (1) for each successive frame pair, the entire image can be reconstructed (Fig. 2).


Minutiae extraction during the sweeping

Each fingerprint is a ridge-valley flow pattern with a unique distribution of discontinuities (minutiae). A common approach to quantify the degree of similarity between two fingerprints is to localize these discontinuities [5] and, then, perform a non-rigid point matching [6]. In 1) The

amount of overlap depends on the finger movement speed and the sensor frame rate. Commonly, commercial sensors present a frame rate, so that the finger can sweep with a speed between 25 and 50 cm/s with an overlap between two consecutive frames of around 90%.

Figure 2: Image obtained with the Atmel [1] sensor and reconstructed with the presented algorithm. The upper image represents 120 frames (out of 250) and the lower one is the entire reconstructed fingerprint. Notice that, for space reason, the images do not have the same scale.

general, the minutiae extraction is performed by binarizing and thinning the image. Moreover, due to the presence of different types of noise, the image needs normally an enhancement of the ridge-valley pattern. In the literature, many methods have been proposed to process a fingerprint image. For an overview of these methods, refer to [5]. Until now, the standard procedure used with the sweep sensors is the extraction of minutiae after the image reconstruction. Here, we propose to extract minutiae from each frame during the sweeping. In this way, we need to store only two consecutive slices for the image processing, using the (1) just to estimate the finger displacement. To quantify the quality of this approach, we compare the minutiae obtained on-the-fly against the minutiae extracted after the reconstruction (off-line) for the same fingerprint. This is done, since we want to achieve the same extraction performances than the off-line procedure. To extract minutiae from an already reconstructed image, we use the approach presented in [4]. The on-the-fly procedure needs, instead, some tricks, due mainly to the reduced frame dimensions2) . Since we process small frames, we exclude from the algorithm by Jain et al. [4] the background removing step and the image enhancement is performed convolving the Gabor filters directly with the entire frame, without decomposing it into blocks. Once the minutiae are extracted from two frames, we use the (1) to compute the displacement [∆x, ∆y] and compute the relative position of the minutiae of the current frame with respect to successive one. Repeating this procedure for each frame pair, we obtain the entire minutiae set. We ran a certain number of trials and, for each fingerprint, we count the number of common 2) In

our experiment, we use the Atmel sensor [1] having a frame size of 8 Ă— 280 pixels

minutiae, the number of minutiae lost during the sweeping and the number of minutiae extracted only during the sweeping. In the Table 1, we report the results of 10 experiments (10 fingerprints). As one can see, we reach the same performances of the off-line method what justifies the use of our algorithm also for feature extraction on-the-fly. Table 1: Number of minutiae extracted from 10 fingerprints processed with the on-the-fly and the off-line procedure. The first raw is the number of minutiae that the two procedures have in common. The second row represents the number of minutiae generated only by the on-the-fly procedure (false positive). The last row represents the number of minutiae appearing only in the reconstructed image (false negative for the on-the-fly procedure).

common minutiae 38 22 12 27 27 38 19 16 24 33 false positive 5 7 2 3 8 4 5 4 2 7 false negative 3 3 0 3 1 7 5 8 3 6


Conclusions and future work

We presented an algorithm for reconstructing fingerprint images obtained by sweep sensors. Besides, we showed how it is possible to extract minutiae during the finger sweeping, reducing the amount of used memory and having the same performance of the off-line procedure. To improve the algorithm performance reducing the extraction errors, we suggest as future work to implement the (1) with sub-pixel accuracy.

References [1] Atmel: - fujitsu:, - st microelectronics: [2] P. Anandan. Measuring Visual Motion from Image Sequences. PhD thesis. Computer Science Department, University of Massachusetts, 2002. [3] International Biometrics Gruop. Biometrics market and industry report 2004-2008. Technical report, 2003. [4] A. K. Jain, L. Hong, and R. M. Bolle. On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):302–314, 1997. [5] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar. Handbook of Fingerprint Recognition. Springer Verlag, June 2003. [6] G. Parziale and A. Niel. A fingerprint matching algorithm based on minutiae triangulation. In Proc. Int. Conf. on Biometrics Authentication, Hong Kong, China, July 2004.

• What is the original contribution of the work? This is the first work presenting the reconstruction of images generated by sweep sensors and, more important, the minutiae extraction during the sweeping. • Why should this contribution be considered important? Until now, there was no possibility to extract minutiae during the sweeping and compare this procedure with the extraction after the image reconstruction. • What is the most closely related work by others and how does this work differ? In the Handbook of Fingerprint Recognition, Maltoni, Maio, Jain and Prabhakar give an overview of the sweep sensors and a possible approach for the image reconstruction, but they do not consider the possibility to extract minutiae during the sweeping. • How can other researchers make use of the results of this work? Sweep sensors have been especially designed for mobile and handheld devices (PDAs, mobile phones, laptop), on which the available physical space and the memory are limited. The possibility to extract minutiae during the sweeping, without storing all the frame set, leads to a reduction of memory usage that is very important for that kind of devices. • Has this work been presented/submitted elsewhere? No • Which form of presentation is preferred: Oral or Poster? Oral • Are you eligible for the best paper award (researcher without a PhD or with the paper about the just finished thesis)? Yes

Image Reconstruction  

To abruptly reduce the production costs of fingerprint systems and push up their wide-spreading, some chip-makers introduced on the market n...

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