Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

Sensors

Tao Qiu¹  Chang Wen¹  Jing Li²  Fang-Qing Wen²  Kai Xie² 

[1] School of Computer Science, Yangtze University, Jingzhou 434023, China;School of Electronic and Information, Yangtze University, Jingzhou 434023, China;

关键词: face recognition;    convolutional neural network;    color 2-dimensional principal component analysis;    decision-level fusion;    normalization;    layered activation function;    probabilistic max-pooling;   

DOI  :  10.3390/s18072080

来源: DOAJ

【 摘 要 】

Given that facial features contain a wide range of identification information and cannot be completely represented by a single feature, the fusion of multiple features is particularly significant for achieving a robust face recognition performance, especially when there is a big difference between the test sets and the training sets. This has been proven in both traditional and deep learning approaches. In this work, we proposed a novel method named C2D-CNN (color 2-dimensional principal component analysis (2DPCA)-convolutional neural network). C2D-CNN combines the features learnt from the original pixels with the image representation learnt by CNN, and then makes decision-level fusion, which can significantly improve the performance of face recognition. Furthermore, a new CNN model is proposed: firstly, we introduce a normalization layer in CNN to speed up the network convergence and shorten the training time. Secondly, the layered activation function is introduced to make the activation function adaptive to the normalized data. Finally, probabilistic max-pooling is applied so that the feature information is preserved to the maximum extent while maintaining feature invariance. Experimental results show that compared with the state-of-the-art method, our method shows better performance and solves low recognition accuracy caused by the difference between test and training datasets.

【 授权许可】

Unknown