Robust face recognition via non‐linear correlation filter bank

Abstract

The author proposes a robust face recognition algorithm called non-linear correlation filter bank (NCFB). NCFB combines the benefits of a sigmoid function such as non-linearities of image pixels and correlation filters (CFs) to achieve better recognition performance. The sigmoid function is used in the spatial domain to extend the uniform dynamic range for enhancing the image contrast. Greyscale images are divided into non-overlapping regions. CFs are designed based on the unconstrained minimum average correlation energy corresponding to each sub-region of images to optimise the overall correlation outputs. NCFB not only takes the differences among face sub-regions into account but also effectively exploits the discriminative information in face sub-regions. The author shows that the proposed method is robust against illumination, pose, and facial expression variations. Experimental results obtained on labelled faces in the wild, Yale B, AR, and FERET face databases demonstrate that the recognition rate in the proposed method is improved compared with other CFs.