Abstract
In this paper, a face recognition method based on deep learning is studied and implemented. By adjusting the hierarchical depth and structure of the typical convolutional neural network model ResNet, a new network model structure is designed, which uses the LFW face detection benchmark. The database is used for confirmatory experiments. The experimental results show that the overall accuracy and model size of the system have a good performance.
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References
Zhang K, Zhang Z, Li Z et al (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503
Roli F, Marcialis GL (2006) Semi-supervised PCA-based face recognition using self-training. Structural, syntactic, and statistical pattern recognition. Springer, Berlin Heidelberg, vol 9, pp 560–568
Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
陈耀丹, 王连明 (2016) 基于卷积神经网络的人脸识别方法. 东北师大学报(自然科学版) 48(02):70–76
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Liu, Y., Yang, J. (2020). Face Recognition Method Based on Convolutional Neural Network. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_233
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DOI: https://doi.org/10.1007/978-981-13-9409-6_233
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Online ISBN: 978-981-13-9409-6
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