Abstract
Face and palm recognition technologies have emerged as powerful tools for authentication, but they can still be vulnerable to fraud and impersonation. Liveness detection is a technique that can detect and prevent fraudulent attempts to bypass authentication by verifying the presence of a live human being during the authentication process. Combining face and palm recognition with liveness detection provides a highly effective and secure approach to authentication, which can prevent fraud and unauthorised access while providing a seamless and user-friendly experience.
References
A. Geitgey, "Face recognition library," Available online: https://github.com/ageitgey/face_recognition
Y. Taigman, et al., "Deep Face Recognition: A Survey," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1701-1708, doi: 10.1109/CVPR.2014.220.
F. Schroff, et al., "FaceNet: A Unified Embedding for Face Recognition and Clustering," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 815-823, doi: 10.1109/CVPR.2015.7298682.
K. He, et al., "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778, doi: 10.1109/CVPR.2016.90.
A. Rathgeb, F. Breitinger, and C. Busch, "Liveness Detection in Biometrics," in IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 844-861, April 2015, doi: 10.1109/TIFS.2015.2414623.
Q. Zhao, et al., "Real-Time Face Recognition with Deep FaceNet," in 2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN), 2017, pp. 682-686, doi: 10.1109/ICCSN.2017.8230254.
A. Das, et al., "Advances in Face Recognition: A Comprehensive Review," in IET Biometrics, vol. 8, no. 1, pp. 9-50, Jan. 2019, doi: 10.1049/iet-bmt.2017.0225.
X. Tan, et al., "Face Detection and Recognition Using Deep Learning: A Survey," in International Journal of Pattern Recognition and Artificial Intelligence, vol. 33, no. 01, 2019, 1942002, doi: 10.1142/S0218001419420021.
Y. Taigman, et al., "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1701-1708, doi: 10.1109/CVPR.2014.220.
Z. Liu, et al., "Large-scale CelebFaces Attributes (CelebA) Dataset," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 5045-5053, doi: 10.1109/CVPR.2015.7299102.
R. Rothe, R. Timofte, and L. Van Gool, "Age and Gender Estimation of Unfiltered Faces," in IEEE International Conference on Computer Vision (ICCV), 2015, pp. 387-391, doi: 10.1109/ICCV.2015.56.
Y. Taigman, et al., "DeepFace: Closing the Gap to Human-Level Performance in Face Verification," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1701-1708, doi: 10.1109/CVPR.2014.220.
Amos, et al., "OpenFace: A General-Purpose Face Recognition Library with Mobile Applications," in CMU-CS-16-118, CMU School of Computer Science, 2016, Available online: http://cmusatyalab.github.io/openface/
X. Cao, Y. Wei, F. Wen, and J. Sun, "Face Alignment by Explicit Shape Regression," in International Journal of Computer Vision, vol. 107, no. 2, pp. 177-190, 2014, doi: 10.1007/s11263-013-0676-x.
X. Wu, et al., "LightCNN: Deeply Learning Face Representations," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1288-1296, doi: 10.1109/ICCV.2015.156.
Jain, A.K., Ross, A. and Nandakumar, K. (2016) ‘Palmprint recognition and its applications’, Springer International Publishing, Switzerland, doi: 10.1007/978-3-319-29854-2_2.