Abstract
Face detection and recognition plays an important role in many occasions. This study explored the application of convolutional neural network in face detection and recognition. Firstly, convolutional neural network was briefly analyzed, and then a face detection model including three convolution layers, four pooling layers, introduction layers and three fully connected layers was designed. In face recognition, the self-learning convolutional neural network (CNN) model for global and local extended learning and Spatial Pyramid Pooling (SPP)-NET model were established. LFW data sets were used as model test samples. The results showed that the face detection model had an accuracy rate of 99%. In face recognition, the self-learning CNN model had an accuracy rate of 94.9% accuracy, and the SPP-Net model had an accuracy rate of 92.85%. It suggests that the face detection and recognition model based on convolutional neural network has good accuracy, and the face recognition efficiency of self-learning CNN model was better, which deserves further research and promotion.
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REFERENCES
Ghiass, R.S., Arandjelovic, O., Bendada, H., et al., Infrared face recognition: A comprehensive review of methodologies and databases, Pattern Recognit., 2014, vol. 47, no. 9, pp. 2807–2824.
He, R., Wu, X., Sun, Z., et al., Wasserstein CNN: Learning invariant features for NIR-VIS face recognition, IEEE Trans. Pattern Anal. Mach. Intell., 2017, no. 99, p. 1.
Hu, S., Choi, J., Chan, A.L., et al., Thermal-to-visible face recognition using partial least squares, J. Optic. Soc. Am. A Opt. Image Sci. Vision, 2015, vol. 32, no. 3, pp. 431–442.
Peng, Y., Wang, S., Long, X., et al., Discriminative graph regularized extreme learning machine and its application to face recognition, Neurocomputing, 2015, vol. 149, pp. 340–353.
Shi, X., Yang, Y., Guo, Z., et al., Face recognition by sparse discriminant analysis via joint L2,1-norm minimization, Pattern Recognit., 2014, vol. 47, no. 7, pp. 2447–2453.
Prakash, N., and Singh, Y., Fuzzy support vector machines for face recognition: A review, Int. J. Comp. Appl., 2015, vol. 131, no. 3, pp. 24–26.
Ding, C., Choi, J., Tao, D., et al., Multi-directional multi-level dual-cross patterns for robust face recognition, IEEE Trans. Pattern Anal. Mach. Intell., 2014, vol. 38, no. 3, pp. 518–531.
Lai, J., Wang, Y., Zhou, G., et al., A fast (l)1-solver and its applications to robust face recognition, J. Ind. Manage. Optim., 2017, vol. 8, no. 1, pp. 163–178.
Zhang, L., Zhou, W.D., and Li, F.Z., Kernel sparse representation-based classifier ensemble for face recognition, Multimedia Tools Appl., 2015, vol. 74, no. 1, pp. 123–137.
Lei, Y., Bennamoun, M., Hayat, M., et al., An efficient 3D face recognition approach using local geometrical signatures, Pattern Recognit., 2014, vol. 47, no. 2, pp. 509–524.
Zhang, K., Zhang, Z., Li, Z., et al., Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Sign. Proc. Lett., 2016, vol. 23, no. 10, pp. 1499–1503.
Bagherinezhad, H., Rastegari, M., and Farhadi, A., LCNN: Lookup-based convolutional neural network, IEEE Conf. Computer Vision and Pattern Recognition. IEEE Computer Society, 2017, pp. 860–869.
Lavinia, Y., Vo, H.H., and Verma, A., Fusion based deep CNN for improved large-scale image action recognition, IEEE Int. Symp. Multimedia, San Jose, CA, 2017, pp. 609–614.
Schroff, F., Kalenichenko, D., and Philbin, J., FaceNet: A unified embedding for face recognition and clustering, IEEE Conf. Computer Vision and Pattern Recognition. IEEE Computer Society, 2015, pp. 815–823.
Rawat, W., and Wang, Z., Deep convolutional neural networks for image classification: A comprehensive review, Neural Comput., 2017, vol. 29, no. 9, pp. 2352–2449.
Galbally, J., Marcel, S., and Fierrez, J., Biometric antispoofing methods: A survey in face recognition, IEEE Access, 2014, vol. 2, pp. 1530–1552.
Smith, D.F., Wiliem, A., and Lovell, B.C., Face recognition on consumer devices: Reflections on replay attacks, IEEE Trans. Inf. Forensics Secur., 2015, vol. 10, no. 4, pp. 736–745.
Kang, D., Han, H., Jain, A.K., et al., Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching, Pattern Recognit., 2014, vol. 47, no. 1, pp. 3750–3766.
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Kai Kang Comparison of Face Recognition and Detection Models: Using Different Convolution Neural Networks. Opt. Mem. Neural Networks 28, 101–108 (2019). https://doi.org/10.3103/S1060992X19020036
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DOI: https://doi.org/10.3103/S1060992X19020036