Abstract
Convolutional neural network, as a common method of deep learning, is excellent in image recognition and classification performance. As one of the key technologies of biometrics recognition, face recognition is a research hotspot in the field of pattern recognition. Based on the basic model, a multi-layer feature fusion face recognition model structure is proposed. This model performs feature extraction on each convolution pooled feature map in the basic model and fuses the features obtained from each layer as the final face representation features. PCA (Principle Component Analysis), LDA (Linear Discriminate Analysis) and LPP (Locality Preserving Projection) are effective feature extraction methods. We fused them into the basic model to build a multi-layer feature fusion face recognition based on the basic model. Experimental results show that the feature fusion model method is significantly higher in recognition rate than the basic model method. Compared with the basic model, the feature fusion model can extract face features more effectively, thereby improving the face recognition rate.
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Li, C., Li, R. (2021). Deep Learning Classification and Recognition Model Construction of Face Living Image Based on Multi-feature Fusion. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_18
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DOI: https://doi.org/10.1007/978-3-030-62743-0_18
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