Computational Intelligence for Biometric Applications: a Survey
DOI:
https://doi.org/10.47839/ijc.15.1.829Keywords:
Biometrics, Computational Intelligence, Neural Networks, Fingerprint, Iris, Face.Abstract
Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.References
A. K. Jain, P. Flynn and A. A. Ross, Handbook of Biometrics, Springer, 2007.
V. Piuri, F. Scotti and R. Donida Labati, Touchless Fingerprint Biometrics, CRC Press, 2015.
A. Genovese, V. Piuri and F. Scotti, Touchless Palmprint Recognition Systems, Springer, 2014.
D. Maltoni, D. Maio, A. K. Jain and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd edition, Springer, 2009.
A. P. Engelbrecht, Computational Intelligence: an Introduction, John Wiley & Sons, 2007.
C. Alippi, A. Ferrero and V. Piuri, Artificial intelligence for instruments and measurement applications, IEEE Instrumentation & Measurement Magazine, (1) 2 (1998), pp. 9-17.
S. S. Haykin, Neural Networks and Learning Machines, 3rd edition ed., Prentice Hall, 2009.
C. Campbell, An introduction to kernel methods, in Studies in Fuzziness and Soft Computing, Springer, (66) (2001), pp. 155-192.
E. Trillas and L. Eciolaza, Fuzzy Logic, Springer, 2015.
Y.-F. Wang, E. Y. Chang and K. P. Cheng, A video analysis framework for soft biometry security surveillance, in Proceedings of the third ACM International Workshop on Video Surveillance & Sensor Networks VSSN'05, 2005.
H. A. Rowley, S. Baluja and T. Kanade, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, (20) 1 (1998), pp. 23-38.
H. Li, Z. Lin, X. Shen, J. Brandt and G. Hua, A convolutional neural network cascade for face detection, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR'2015, 2015, pp. 5325-5334.
C.-F. Juang and S.-J. Shiu, Using self-organizing fuzzy network with support vector learning for face detection in color images, Neurocomputing, (71) 16-18 (2008), pp. 3409-3420.
O. Çeliktutan, S. Ulukaya and B. Sankur, A comparative study of face landmarking techniques, EURASIP Journal on Image and Video Processing, (13) 1 (2013), pp. 1-27.
E. Zhu, J. Yin, C. Hu and G. Zhang, A systematic method for fingerprint ridge orientation estimation and image segmentation, Pattern Recognition, (39) 8 (2006), pp. 1452-1472.
A. C. P. Barreto-Marques and A. C. Gay-Thome, A neural network fingerprint segmentation method, in Proceedings of the Fifth International Conference on Hybrid Intelligent Systems HIS'05, 2005, pp. 1-6.
V. Piuri, F. Scotti and R. Donida Labati, Neural-based iterative approach for iris detection in iris recognition systems, in Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defence Applications CISDA'2009, Ottawa, Canada, (July 8-10, 2009), pp. 1-6.
F. Scotti and V. Piuri, Adaptive reflection detection and location in iris biometric images by using computational intelligence techniques, IEEE Transactions on Instrumentation and Measurement, (59) 7 (2010), pp. 1825-1833.
F. Scotti, Computational intelligence techniques for reflections identification in iris biometric images, in Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications CIMSA'2007, Ostuni, Italy, (June 27-29, 2007), pp. 84-88.
T. Rongnian and W. Shaojie, Improving iris segmentation performance via borders recognition, in Proceedings of the Fourth International Conference on Intelligent Computation Technology and Automation (ICICTA'11), vol. 2, 2011, pp. 580-583.
J. Kang and W. Zhang, Fingerprint image segmentation using modified fuzzy c-means algorithm, in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering ICBBE'2009, Beijing, (11-13 June 2009), pp. 1-4.
H. Proenca and L. Alexandre, Iris segmentation methodology for non-cooperative recognition, IEEE Proceedings on Vision, Image and Signal Processing, (153) 2 (2006), pp. 199-205.
R. Donida Labati, A. Genovese, E. Muñoz, V. Piuri, F. Scotti and G. Sforza, Automatic classification of acquisition problems affecting fingerprint images in automated border controls, in Proceedings of the IEEE Symposium Series on Computational Intelligence SSCI'2015, 2015, pp. 354-361.
N. A. Schmid, J. Zuo, F. Nicolo and H. Wechsler, Iris Quality Metrics for Adaptive Authentication, in Handbook of Iris Recognition, M. J. Burge and K. W. Bowyer, Eds., Springer, 2013, pp. 67–84.
E. Tabass, C. W. and C. Watson, NIST Fingerprint Image Quality, NIST, 2004.
M. A. Olsen, E. Tabassi, A. Makarov and C. Busch, Self-organizing maps for fingerprint image quality assessment, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPRW'2013, Portland, OR, (23-28 June 2013), pp. 138-145.
R. Donida Labati, V. Piuri and F. Scotti, Neural-based quality measurement of fingerprint images in contactless biometric systems, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2010, 2010, pp. 1-8.
R. Donida Labati, A. Genovese, V. Piuri and F. Scotti, Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2012, 2012, pp. 1-8.
J. R. Beveridge, D. S. Bolme, B. A. Draper, G. H. Givens, Y. M. Lui and P. J. Phillips, Quantifying how lighting and focus affect face recognition performance, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPRW'2010, 2010, pp. 74-81.
B. F. Klare and A. K. Jain, Face recognition: Impostor-based measures of uniqueness and quality, in Proceedings of the IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems BTAS'2012, Arlington, VA, (23-27 Sept. 2012), pp. 237-244.
S. Bharadwaj, M. Vatsa and R. Singh, Can holistic representations be used for face biometric quality assessment?, in Proceedings of the IEEE International Conference on Image Processing ICIP'2013, Melbourne, Australia, (September 15-18, 2013), pp. 2792-2796.
D. Gragnaniello, C. Sansone and L. Verdoliva, Iris liveness detection for mobile devices based on local descriptors, Pattern Recognition Letters, (57) (2015), pp. 81-87.
J. Jang, K. R. Park, J. Kim and Y. Lee, New focus assessment method for iris recognition systems, Pattern Recognition Letters, (29) 13 (2008), pp. 1759-1767.
M. Sahasrabudhe and A. M. Namboodiri, Fingerprint enhancement using unsupervised hierarchical feature learning, in Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing ICVGIP'14, 2014.
L. Ji, Z. Yi, L. Shang and X. Pu, Binary fingerprint image thinning using template-based PCNNs, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), (37) 5 (2007), pp. 1407-1413.
R. Donida Labati, A. Genovese, V. Piuri and F. Scotti, Contactless fingerprint recognition: A neural approach for perspective and rotation effects reduction, in Proceedings of the IEEE Workshop on Computational Intelligence in Biometrics and Identity Management CIBIM'2013, 2013, pp. 22-30.
R. Donida Labati, A. Genovese, V. Piuri and F. Scotti, Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques, in Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications CIMSA'2010, 2010, pp. 18-23.
K. Cao and A. K. Jain, Latent orientation field estimation via convolutional neural network, in Proceedings of the 8th IAPR International Conference on Biometrics ICB'2015, 2015, pp. 349-356.
M. Song, D. Tao, X. Huang, C. Chen and J. Bu, Three-dimensional face reconstruction from a single image by a coupled RBF Network, IEEE Transactions on Image Processing, (21) 5 (2012), pp. 2887-2897.
S. H. Moi, H. Asmuni, R. Hassan and R. M. Othman, A unified approach for unconstrained off-angle iris recognition, in Proceedings of the International Symposium on Biometrics and Security Technologies ISBAST'2014, Kuala Lumpur, (26-27 Aug. 2014), pp. 39-44.
G. Xu, Z. Zhang and Y. Ma, A novel method for iris feature extraction based on intersecting cortical model network, Journal of Applied Mathematics and Computing, (26) 1 (2005), pp. 341-352.
A. Genovese, E. Muñoz, V. Piuri, F. Scotti and G. Sforza, Towards touchless pore fingerprint biometrics: a neural approach, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2016, Vancouver, Canada, (July 24-29, 2016), accepted.
S. Gao, Y. Zhang, K. Jia, J. Lu and Y. Zhang, Single sample face recognition via learning deep supervised autoencoders, IEEE Transactions on Information Forensics and Security, (10) 10 (2015), pp. 2108-2118.
C. Ding and D. Tao, Robust face recognition via multimodal deep face representation, IEEE Transactions on Multimedia, (17) 11 (2015), pp. 2049-2058.
C. Xiong, L. Liu, X. Zhao, S. Yan and T. K. Kim, Convolutional fusion network for face verification in the wild, IEEE Transactions on Circuits and Systems for Video Technology, (26) 3 (2016), pp. 517-528.
J. Zhang, S. Shan, M. Kan and X. Chen, Coarse-to-fine auto-encoder networks (CFAN) for real-time face alignment, in Proceedings of the 13th European Conference Computer Vision ECCV'2014, Zurich, Switzerland, (September 6-12, 2014), Part II, pp. 1-16.
Y. Sun, X. Wang and X. Tang, Hybrid deep learning for face verification, in Proceedings of the IEEE International Conference on Computer Vision ICCV'2013, (1-8 Dec. 2013), pp. 1489-1496.
N. Liu, M. Zhang, H. Li, Z. Sun and T. Tan, DeepIris: Learning pairwise filter bank for heterogeneous iris verification, Pattern Recognition Letters, 2015, in press.
A. Asthana, S. Zafeiriou, S. Cheng and M. Pantic, Incremental face alignment in the wild, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR'2014, (23-28 June 2014), pp. 1859-1866.
X. Song, Y. Zheng, X. Wu, X. Yang and J. Yang, A complete fuzzy discriminant analysis approach for face recognition, Applied Soft Computing, (10) 1 (2010), pp. 208-214.
K. Roy and P. Bhattacharya, Level set approaches and adaptive asymmetrical SVMs applied for nonideal iris recognition, in Image Analysis and Recognition, M. K. and A. Campilho, Eds., Springer, 2009, pp. 418-428.
P. Mansukhani and V. Govindaraju, Selecting optimal classification features for SVM based elimination of incorrectly matched minutiae, in Proceedings of SPIE 6944, Biometric Technology for Human Identification V, 2008.
X. Chen, J. Tian and X. Yang, A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure, IEEE Transactions on Image Processing, (15) 3 (2006), pp. 767-776.
T. Kristensen, J. Borthen and K. Fyllingsnes, Comparison of neural network based fingerprint classification techniques, in Proceedings of the International Joint Conference on Neural Networks IJCNN'2007, (12-17 Aug. 2007), pp. 1043-1048.
I. El-Feghi, A. Tahar and M. Ahmadi, Efficient features extraction for fingerprint classification with multilayer perceptron neural network, in Proceedings of the 10th International Symposium on Signals, Circuits and Systems ISSCS'2011, (June 30-July 1, 2011), pp. 1-4.
S. Kang, D. Lee and C. D. Yoo, Face attribute classification using attribute-aware correlation map and gated convolutional neural networks, in Proceedings of the IEEE International Conference on Image Processing ICIP'2015, (27-30 Sept. 2015), pp. 4922-4926.
L. Nasseri, A. A. B. Shirazi and N. Sadeghigol, Tsallis entropy, PCA and neural network in novel algorithm of iris classification, in Proceedings of the World Congress on Information and Communication Technologies WICT'2011, (11-14 Dec. 2011), pp. 385-390.
G. T. Candela, P. J. Grother, C. I. Watson, R. A. Wilkinson and C. L. Wilson, PCASYS - A Pattern-Level Classification Automation System for Fingerprints, NIST Internal Report - 5647, 1995.
M. I. Fanany, M. Ohno and I. Kumazawa, A scheme for reconstructing face from shading using smooth projected polygon representation NN, in Proceedings of International Conference on Image Processing ICIP'2002, 2002, vol. 2, pp. 305-308.
A. Rattani, F. Roli and E. Granger, Adaptive Biometric Systems, Springer, 2015.
M. He, S.-J. Horng, P. Fan, R.-S. Run, R.-J. Chen, J.-L. Lai, M. K. Khan and K. O. Sentosa, Performance evaluation of score level fusion in multimodal biometric systems, Pattern Recognition, (43) 5 (2010), pp. 1789-1800.
F. Scotti, A. Azzini, S. Marrara and R. Sassi, A fuzzy approach to multimodal biometric continuous authentication, Fuzzy Optimization and Decision Making, (7) 3 (2008), pp. 243-256.
N. Poh, M. Tistarelli and Y. Sun, On the use of discriminative cohort score normalization for unconstrained face recognition, IEEE Transactions on Information Forensics and Security, (9) 12 (2014), pp. 2063-2075.
N. Poh and J. Kittler, Incorporating Model-specific score distribution in speaker verification systems, IEEE Transactions on Audio, Speech, and Language Processing, (16) 3 (2008), pp. 594-606.
B. Tan and S. Schuckers, Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise, Pattern Recognition, (43) 8 (2010), pp. 2845-2857.
K. Kollreider, H. Fronthaler and J. Bigun, Non-intrusive liveness detection by face images, Image and Vision Computing, (27) 3 (2009), pp. 233-244.
K. Kollreider, H. Fronthaler, M. I. Faraj and J. Bigun, Real-time face detection and motion analysis with application in liveness assessment, IEEE Transactions on Information Forensics and Security, (2) 3 (2007), pp. 548-558.
X. Huang, C. Ti, Q.-z. Hou, A. Tokuta and R. Yang, An experimental study of pupil constriction for liveness detection, in Proceedings of the IEEE Workshop on Applications of Computer Vision WACV'2013, (Jan. 15-17, 2013), pp. 252-258.
J. Määttä, A. Hadid and M. Pietikainen, Face spoofing detection from single images using micro-texture analysis, in Proceedings of the International Joint Conference on Biometrics IJCB'2011, (Oct. 11-13, 2011), pp. 1-7.
A. Rattani and A. Ross, Automatic adaptation of fingerprint liveness detector to new spoof materials, in Proceedings of the nternational Joint Conference on Biometrics IJCB'2014, (Sept. 29-Oct. 2, 2014), pp. 1-8.
R. Chen, X. Lin and T. Ding, Liveness detection for iris recognition using multispectral images, Pattern Recognition Letters, (33) 12 (2012), pp. 1513-1519.
A. Schuckers and S. Abhyankar, Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques, in Proceedings of the International Conference on Image Processing ICIP'2006, (8-11 Oct. 2006), pp. 321-324.
M. Upmanyu, A. M. Namboodiri, K. Srinathan and C. V. Jawahar, Blind authentication: a secure crypto-biometric verification protocol, IEEE Transactions on Information Forensics and Security, (5) 2 (2010), pp. 255-268.
M. Barni, P. Failla, R. Lazzeretti, A. R. Sadeghi and T. Schneider, Privacy-preserving ECG classification with branching programs and neural networks, IEEE Transactions on Information Forensics and Security, (6) 2 (2011), pp. 452-468.
S. Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2011.
R. Donida Labati, A. Genovese, V. Piuri and F. Scotti, Iris segmentation: state of the art and innovative methods, in Cross Disciplinary Biometric Systems, vol. 37, C. Liu and V. Mago, Eds., Springer, 2012, pp. 151-182.
M. Gamassi, M. Lazzaroni, M. Misino, V. Piuri, D. Sana and F. Scotti, Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement, IEEE Transactions on Instrumentation and Measurement, (54) 4 (2005), pp. 1489-1496.
F. Alonso-Fernandez, J. Fierrez and J. Ortega-Garcia, Quality Measures in Biometric Systems, IEEE Security Privacy, (10) 6 (2012), pp. 52-62.
L. Lumini and A. Nanni, Descriptors for image-based fingerprint matchers, Expert Systems with Applications, (36) 10 (2009), pp. 12414-12422.
W. Dong, Z. Sun and T. Tan, Iris matching based on personalized weight map, IEEE Transactios on Pattern Analysis and Machine Intelligence, (33) 9 (2011), pp. 1744-1757.
M. De Marsico, A. Petrosino and S. Ricciardi, Iris recognition through machine learning techniques: a survey, Pattern Recognition Letters, 2016, in press.
X. Tan, B. Bhanu and Y. Lin, Fingerprint classification based on learned features, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), (35) 3 (2005), pp. 287-300.
A. Ross, Multibiometrics, in Encyclopedia of Cryptography and Security, Second Edition, C. A. Henk and S. Jajodia, Eds., Springer, 2011, pp. 967-973.
A. Ross, A. K. Jain and K. Nandakumar, Handbook of Multibiometrics, Springer, 2006.
S. Singh, A. Gyaourova, G. Bebis and I. Pavlidis, Infrared and visible image fusion for face recognition, in Proceedings of SPIE 5404, 2004.
N. K. Ratha and A. K. Jain, Infrared and visible image fusion for face recognition, in Proceedings of SPIE 5404, 2004.
N. Alajlan, N. Ammour and M. S. Islam, Fusion of fingerprint and heartbeat biometrics using fuzzy adaptive genetic algorithm, in Proceedings of the World Congress on Internet Security WCIS'2013, (9-12 Dec. 2013), pp. 76-81.
N. Srinivas, K. Veeramachaneni, L. Osadciw and A. Ross, Decision-level fusion strategies for correlated biometric classifiers, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW'2008, (23-28 June 2008), pp. 1-6.
A. Ross, A. Jain and K. Nandakumar, Score normalization in multimodal biometric systems, Pattern Recognition, (38) 12 (2005), pp. 2270- 2285.
R. Donida Labati, A. Genovese, E. Muñoz, F. Scotti, V. Piuri and G. Sforza, Advanced design of automated border control gates: biometric system techniques and research trends, in Proceedings of the IEEE International Symposium on Systems Engineering ISSE'2015, (28-30 Sept. 2015), pp. 412-419.
O. Kähm and N. Damer, 2D face liveness detection: An overview, in Proceedings of the International Conference on Biometrics Special Interest Group BIOSIG'2012, (6-7 Sept. 2012), pp. 1-12.
E. Marasco and A. Ross, A survey on antispoofing schemes for fingerprint recognition systems, ACM Computing Surveys, (47), 2 (2014), pp. 1-36.
H. Wei, L. Chen and J. Ferryman, Biometrics in ABC: counter-spoofing research, in Proceedings of the Frontex Global Conference on Future Developments of Automated Border Control, (10–11 October, 2013), pp. 54-59.
R. Donida Labati, V. Piuri and F. Scotti, Biometric privacy protection: guidelines and technologies, in E-Business and Telecommunications, Springer, 2012, pp. 3-19.
S. Cimato, M. Gamassi, V. Piuri, R. Sassi and F. Scotti, Privacy-aware biometrics: design and implementation of a multimodal verification system, in Proceedings of the Annual Computer Security Applications Conference ACSAC'2008, (Dec. 8-12, 2008), pp. 130-139.
V. Ciriani, S. De Capitani di Vimercati, S. Foresti and P. Samarati, Microdata Protection, in Secure Data Management in Decentralized Systems, T. Yu and S. Jajodia, Eds., Springer, 2007, pp. 291-321.
S. De Capitani di Vimercati, S. Foresti, G. Livraga and P. Samarati, Data privacy: definitions and techniques, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, (20) 6 (2012), pp. 793-817.
M. Barni, T. Bianchi, D. Catalano, M. Di Raimondo, R. Donida Labati, P. Failla, D. Fiore, R. Lazzeretti, V. Piuri and A. Piva, A privacy-compliant fingerprint recognition system based on homomorphic encryption and fingercode templates, in Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems BTAS'2010, 2010, pp. 1-7.
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