Face recognition under varying illuminations with multi-scale gradient maximum response

Illumination variations significantly affect the performance of face recognition systems. This paper presents a multi-scale method based on the maximum response (MR) filter bank and the gradient of faces. The proposed method first scales the face image using a simple log function to expand darker pixels and compress brighter pixels. It then effectively employs a subset of the MR filter bank to enhance edges and partially reduce illumination. Finally, it applies an enhanced multi-scale Gradientface method, which increases discriminating abilities and captures different characteristics of the face image to produce illumination invariant feature representation. Our extensive experiments on four closed-universe face databases and one open-universe database show the proposed method achieves the best recognition accuracy when comparing with 14 recently proposed state-of-the-art methods and its four variant methods. Our evaluations using receiver operating characteristic (ROC) curves on the four closed-universe face databases and precision and recall (PR) curves on the open-universe face database also verify the proposed method has the best verification and discrimination ability compared with other peer methods.