A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

Abstract

Purpose

In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.

Design/methodology/approach

The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.

Findings

Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.

Originality/value

The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

Citation

Wei, M. (2020), "A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network", International Journal of Intelligent Computing and Cybernetics, Vol. 13 No. 2, pp. 207-221. https://doi.org/10.1108/IJICC-02-2020-0017