Patch-Wise Partial Face Recognition Using Convolutional Neural Network
Tóm tắt
Automatic face recognition still suffers from some problems in the real-world scenarios such as occlusion. Hence, identifying the face from its partial appearance is a challenging issue as yet. To address this, issue many methods have been proposed using traditional feature extraction techniques. In this paper, a partial face recognition problem has been tackled through utilizing patch-wise matching with Convolutional Neural Network (CNN). Firstly, a gallery images are divided into local patches, and each patch is regarded as an independent image. Then, AlexNet architecture is utilized for training image patches. The Instance-To-Class (ITC) matching technique using K-Nearest Neighbour (KNN) algorithm specifies the class of the facial test image based on patch prediction. The notable contributions of our work are two-folds: the first one is employing ITC technique for patch prediction and the last one is adopting a deep learning technique for feature extraction and handling partial occlusion problem. The achieved accuracies on two de-facto datasets show that our method outperforms several existing methods that use hand-designed feature descriptors.
Tài liệu tham khảo
Ekenel, H.K. and Stiefelhagen, R., Why Is Facial Occlusion a Challenging Problem?, in Advances in Biometrics, vol. 5558, Tistarelli, M. and Nixon, M.S., Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 299–308.
Wolf, L., Hassner, T., and Maoz, I., Face recognition in unconstrained videos with matched background similarity, in CVPR 2011, Colorado Springs, CO, USA, 2011, pp. 529–534. https://doi.org/10.1109/CVPR.2011.5995566
Taigman, Y., Yang, M., Ranzato, M., and Wolf, L., DeepFace: Closing the Gap to Human-Level Performance in Face Verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1701–1708. https://doi.org/10.1109/CVPR.2014.220
Sun, Y., Wang, X., and Tang, X., Deep Learning Face Representation from Predicting 10 000 Classes, in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014, pp. 1891–1898. https://doi.org/10.1109/CVPR.2014.244
Sun, Y., Liang, D., Wang, X., and Tang, X., DeepID3: Face Recognition with Very Deep Neural Networks, ArXiv150200873 Cs, 2015. Accessed November 26, 2020. http://arxiv.org/abs/1502.00873.
Schroff, F., Kalenichenko, D., and Philbin, J., FaceNet: A unified embedding for face recognition and clustering, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 815–823. https://doi.org/10.1109/CVPR.2015.7298682
Min, R., Hadid, A., and Dugelay, J.-L., Improving the recognition of faces occluded by facial accessories, in Face and Gesture 2011, Santa Barbara, CA, USA, 2011, pp. 442–447. https://doi.org/10.1109/FG.2011.5771439
Khadatkar, A., Khedgaonkar, R., and Patnaik, K.S., Occlusion invariant face recognition system, in 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, India, 2016, pp. 1–4. https://doi.org/10.1109/STARTUP.2016.7583985
Liao, S. and Jain, A.K., Partial face recognition: An alignment free approach, in 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, USA, 2011, pp. 1–8. https://doi.org/10.1109/IJCB.2011.6117573
Phillips, P.J. et al., Overview of the Face Recognition Grand Challenge, in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 2005, vol. 1, pp. 947–954. https://doi.org/10.1109/CVPR.2005.268
Huang, G.B., Mattar, M., Berg, T., and Learned-Miller, E., Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, University of Massachusetts, Amherst, Tech. Report 07-49, October 2007, p. 14.
Hu, J., Lu, J., and Tan, Y.-P., Robust partial face recognition using instance-to-class distance, in 2013 Visual Communications and Image Processing (VCIP), Kuching, Malaysia, 2013, pp. 1–6. https://doi.org/10.1109/VCIP.2013.6706353
Nikan, S. and Ahmadi, M., Partial Face Recognition Based on Template Matching, in 2015 11th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), Bangkok, Thailand, 2015, pp. 160–163. https://doi.org/10.1109/SITIS.2015.19
Weng, R., Lu, J., and Tan, Y.-P., Robust Point Set Matching for Partial Face Recognition, IEEE Trans. Image Process., 2016, vol. 25, no. 3, pp. 1163–1176. https://doi.org/10.1109/TIP.2016.2515987
Georghiades, A.S., Belhumeur, P.N., and Kriegman, D.J., From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 2001, vol. 23, no. 6, pp. 643–660. https://doi.org/10.1109/34.927464
Kumar, N., Berg, A.C., Belhumeur, P.N., and Nayar, S.K., Attribute and simile classifiers for face verification, in 2009 IEEE 12th International Conference on Computer Vision, Kyoto, 2009, pp. 365–372. https://doi.org/10.1109/ICCV.2009.5459250
Patel, T. and Shah, B., A Survey on facial feature extraction techniques for automatic face annotation, International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 2017, p. 5.
Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, Commun. ACM, 2017, vol. 60, no. 6, pp. 84–90. https://doi.org/10.1145/3065386
Zeiler, M.D., ADADELTA: An Adaptive Learning Rate Method, ArXiv12125701 Cs, 2012. Accessed November 26, 2020. http://arxiv.org/abs/1212.5701.
Kingma, D.P. and Ba, J., Adam: A Method for Stochastic Optimization, ArXiv14126980 Cs, 2017. Accessed November 26, 2020. http://arxiv.org/abs/1412.6980.
Dozat, T., Incorporating Nesterov Momentum into Adam, Workshop Track – ICLR, 2016, pp. 1–4.
Ruder, S., An overview of gradient descent optimization algorithms, ArXiv160904747 Cs, 2017. Accessed November 26, 2020. http://arxiv.org/abs/1609.04747.
Yi Huang, Dong Xu, and Tat-Jen Cham, Face and human gait recognition using image-to-class distance, IEEE Trans. Circuits Syst. Video Technol., 2010, vol. 20, no. 3, pp. 431–438. https://doi.org/10.1109/TCSVT.2009.2035852