Abstract
Face recognition technologies are commonly used in access control systems. It is done by extracting selected features from the face image, taken by a 2D camera. This technique lacks the case of a picture placed in front of the camera. The system will mistakenly recognize it as a real live person and so, allow the access of the picture holder, which may be an unauthorized person. A new generation of security systems uses a three-dimensional face recognition. Although it is better than 2D, it lacks a similar case, where a 3D image is generated from many 2D images. The system will assume it is a picture taken from a live person, and mistakenly, allow the access. We propose an enhancement to the existing authentication process given 2D face image. It is based on inner images extracted from a hyperspectral camera. These images represent inner layers of the person tissue structure, which in general are different from person to person and so, may be used to differentiate between two persons. We use these generated features to generate an authentication signature. The authentication signature is a composition of processed inner layers features. To prove that this signature is universally unique and can substitute the current use of 2D image recognition system, there is a need to conduct a comprehensive testing and apply other technologies to prove it. We are not at this stage. Therefore, at this stage, we propose adding to each image a unique signature generated from the corresponding hyperspectral inner layers. When a person is trying to access, the access control system, using a hyperspectral camera, captures its standard image features, and in addition, calculates the inner images to generate a relatively unique signature, and compares both elements to the identification table. Experiments show that this combination generates a relatively unique identification key. From the beginning of our initial experiments, it kept its attentiveness and uniqueness for all we tried to challenge it. Further experiments prove the significant contribution of inner features for strengthening the person authentication.
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References
Z. Pan, G. Healey, M. Prasad, B. Tromberg, Face recognition in hyperspectral images. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1552–1560 (2003)
L. Denes, P. Metes, Y. Liu, Hyperspectral face database. Technical Report CMU-RI-TR-02-25, 2002
Y. Chou, P. Bajcsy, Toward face detection, pose estimation and human recognition from hyperspectral imagery. Technical Report NCSA-ALG-04-0005, 2004. http://isda.ncsa.uiuc.edu/peter/ [online]
H. Chang, H. Harishwaran, M. Yi, A. Koschan, B. Abidi, M. Abidi, An indoor and outdoor, multimodal, multispectral and multi-illuminant database for face recognition, in Proceedings of CVPR2006, Workshop on Multi-model Biometrics, June 2006
B. Guo, S. Gunn, R. Damper, J. Nelson, Band selection for hyperspectral image classification using mutual information. IEEE Geosci. Remote Sens. Lett. 3(4), 522–526 (2006)
R. Huang, M. He, Band selection based on feature weighting for classification of hyperspectral data. IEEE Geosci. Remote Sens. Lett. 2(2), 156–159 (2005)
W. Cho, J. Jang, A. Koshan, M. Abidi, J. Paike, Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models. Opt. Express 24(24) (2016)
A. Ghasemzadeh, H. Demirel, Hyperspectral face recognition using 3D discrete wavelet transform, in IEEE Xplore, Image Processing Theory Tools and Applications (IPTA), 2017. https://doi.org/10.1109/ipta.2016.7821008
V. Sharma, A. Diba, T. Tuytelaars, L. Van Gool, Hyperspectral CNN for image classification & band selection, with application to face recognition. Technical Report: KUL/ESAT/PSI/1604, 12/2016
G. Chen, W. Sun, W. Xie, Hyperspectral face recognition with log-polar Fourier features and collaborative representation based voting classifiers. IET Digit. Libr. 6(1), 36–42 (2017). https://doi.org/10.1049/iet-bmt.2015.0103. Print ISSN 2047-4938
T. Skaulia, J. Farrell, A collection of hyperspectral images for imaging systems research. Proceedings Volume 8660, Digital Photography IX; 86600C (2013). https://doi.org/10.1117/12.2007097. IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States. SPIE.digital.library 03-Feb-2013
Acknowledgements
The Hyperspectral images appear in this paper have been taken from Torbjørn Skaulia and Joyce Farrell paper [11]. We are also grateful to the subjects who have consented to publishing their image.
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Leshem, G., Domb, M. (2020). Face Authentication Using Image Signature Generated from Hyperspectral Inner Images. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1041. Springer, Singapore. https://doi.org/10.1007/978-981-15-0637-6_9
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DOI: https://doi.org/10.1007/978-981-15-0637-6_9
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