Bi-directional CRC algorithm using CNN-based features for face classification

The Journal of Engineering

Xiaoning Song¹  Yanan Wang²  Tian Na³ 

[1] Centre for Vision, Speech and Signal Processing, University of Surrey , Guildford , UK;School of Internet of Things Engineering, Jiangnan University , Wuxi , People's Republic of China

关键词: bi-directional optimisation problem;    convolutional neural network-based features;    deep convolutional neural network;    bi-directional representation model;    collaborative representation-based classification;    face classification;    robust face recognition;    CNN-based features;    bi-directional CRC algorithm;    fast reverse representation model;   

DOI  :  10.1049/joe.2018.8304

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

Collaborative representation-based classification (CRC) has become a breakthrough in face classification due to its distinguished collaborative capacity. Nevertheless, insufficient observations of per subject are usually offered by few or even a single gallery image for face classification, which lead to a sensitive response to the variations from the original data set. In this study, the authors present a bi-directional CRC algorithm using convolutional neural network-based features for face classification. They first employ a deep convolutional neural network to extract facial features from the original gallery and query sets, and then develop a fast reverse representation model to obtain the auxiliary residual information between each training sample and the reconstructed one that is achieved from the test sample. Secondly, they offer a new solution to the bi-directional optimisation problem by which the input sample is well represented by the forward linear combination and the reverse one, respectively. The last contribution is to utilise a competitive fusion method for robust face recognition, which weighted reconstructed residuals from the bi-directional representation model. Experimental results obtained from a set of well-known face databases including AR, FERET, and ORL verify the validity of the proposed method, especially in the robustness to small sample size problem.

【 授权许可】

CC BY