Deep Learning in Biometrics: A Survey


Deep learning has been established in the last few years as the gold standard for data processing, achieving peak performance in image, text and audio understanding. At the same time, digital security is of utmost importance in this day and age, where everyone could get into our personal devices like cellphones or laptops, where we store our most valuable information. One of the possible ways to prevent this is via advanced and personalized security: biometrics. In this survey, it is considered how the scientific advances in the field of deep learning are applied to biometrics in order to enhance the protection of our data.
  • Referencias
  • Cómo citar
  • Del mismo autor
  • Métricas
Abdullayeva, F., Imamverdiyev, Y., Musayev, V., and Wayman, J., 2008. Analysis of security vulnerabilities in biometric systems. In The second international conference: problems of cybernetics and informatics.

Arora, S. and Bhatia, M. S., 2019. Fingerprint Spoofing Detection to Improve Customer Security in Mobile Financial Applications Using Deep Learning. Arabian Journal for Science and Engineering, pages 1-17. Bowyer, K. W. and Flynn, P. J., 2016. Biometric identification of identical twins: A survey. In 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pages 1-8. ISSN null. doi:10.1109/BTAS.2016.7791176. -

Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., and Shah, R., 1994. Signature verification using a "siamese" time delay neural network. In Advances in neural information processing systems, pages 737-744. -

Burger, B., Fuchs, D., Sprecher, E., and Itin, P., 2011. The immigration delay disease: Adermatoglyphia-inherited absence of epidermal ridges. Journal of the American Academy of Dermatology, 64(5):974-980. -

Darve, N. R. and Theng, D. P., 2015. Comparison of biometric and non-biometric security techniques in mobile cloud computing. In 2015 2nd International Conference on Electronics and Communication Systems (ICECS), pages 213-216. IEEE. -

Denker, J. S., Gardner, W., Graf, H. P., Henderson, D., Howard, R. E., Hubbard, W., Jackel, L. D., Baird, H. S., and Guyon, I., 1989. Neural network recognizer for hand-written zip code digits. In Advances in neural information processing systems, pages 323-331.

Gautam, G., Raj, A., and Mukhopadhyay, S., 2019. Identifying twins based on ocular region features using deep representations. Applied Intelligence, pages 1-18. -

Ghiani, L., Yambay, D. A., Mura, V., Marcialis, G. L., Roli, F., and Schuckers, S. A., 2017. Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015. Image and Vision Computing, 58:110-128. -

Hamza, R., Yan, Z., Muhammad, K., Bellavista, P., and Titouna, F., 2019. A privacy-preserving cryptosystem for IoT E-healthcare. Information Sciences. ISSN 0020-0255. doi: -

He, K., Zhang, X., Ren, S., and Sun, J., 2015. Deep Residual Learning for Image Recognition. CoRR, abs/1512.03385. -

Jang, H.-U., Choi, H.-Y., Kim, D., Son, J., and Lee, H.-K., 2017. Fingerprint spoof detection using contrast enhancement and convolutional neural networks. In International Conference on Information Science and Applications, pages 331-338. Springer. -

Khazaee, A. and Zadeh, A. E., 2014. ECG beat classification using particle swarm optimization and support vector machine. Frontiers of Computer Science, 8(2):217-231. ISSN 2095-2236. doi:10.1007/s11704-014-2398-1. Kim, M.-G. and Pan, S. B., 2019. Deep Learning based on 1-D Ensemble Networks using ECG for Real-Time. User Recognition. IEEE Transactions on Industrial Informatics. -

Koch, G., Zemel, R., and Salakhutdinov, R., 2015. Siamese neural networks for one-shot image recognition. InICML deep learning workshop, volume 2.

Marasco, E., Johnson, P., Sansone, C., and Schuckers, S., 2011. Increase the security of multibiometric systems by incorporating a spoofing detection algorithm in the fusion mechanism. In International Workshop on Multiple Classifier Systems, pages 309-318. Springer. -

Mehta, S. and Lingayat, N., 2008. SVM-based algorithm for recognition of QRS complexes in electrocardiogram. IRBM, 29(5):310 - 317. ISSN 1959-0318. doi: -

Ogiela, M. R. and Ogiela, L., 2016. On Using Cognitive Models in Cryptography. In 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pages 1055-1058. ISSN 1550-445X. doi:10.1109/AINA.2016.159. -

Qibin Zhao and Liqing Zhang, 2005. ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. In 2005 International Conference on Neural Networks and Brain, volume 2, pages 1089-1092. ISSN null. doi:10.1109/ICNNB.2005.1614807. -

Roy, A., Memon, N., and Ross, A., 2017. MasterPrint: Exploring the Vulnerability of Partial Fingerprint-Based Authentication Systems. IEEE Transactions on Information Forensics and Security, 12(9):2013-2025. ISSN 1556-6021. doi:10.1109/TIFS.2017.2691658. -

Schmidhuber, J., 2015. Deep learning in neural networks: An overview. Neural networks, 61:85-117. Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. -

Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V., 2017. Learning Transferable Architectures for Scalable Image Recognition. CoRR, abs/1707.07012. -
Botana López, A. (2019). Deep Learning in Biometrics: A Survey. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 8(4), 19–32.


Download data is not yet available.

Author Biography

Alberto Botana López

Master on Intelligent Systems, University of Salamanca, Spain
Student at Master on Intelligent Systems