Abstract
As one of the non-contact biometrics, face representation had been widely used in many circumstances. However conventional methods could no longer satisfy the demand at present, due to its low recognition accuracy and restrictions of many occasions. In this paper, we presented the deep learning method to achieve facial landmark detection and unrestricted face recognition. To solve the face landmark detection problem, this paper proposed a layer-by-layer training method of a deep convolutional neural network to help the convolutional neural network to converge and proposed a sample transformation method to avoid over-fitting. This method had reached an accuracy of 91% on ORL face database. To solve the face recognition problem, this paper proposed a SIAMESE convolutional neural network which was trained on different parts and scales of a face and concatenated the face representation. The face recognition algorithm had reached an accuracy of 91% on ORL and 81% on LFW face database.
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Wang, W., Yang, J., Xiao, J., Li, S., Zhou, D. (2015). Face Recognition Based on Deep Learning. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_73
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DOI: https://doi.org/10.1007/978-3-319-15554-8_73
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