Using statistical characteristics of gradient phases for robust face recognition under illumination variations

Gradient phase, which is treated as an illumination insensitive measure, is an important feature for visual detection and recognition applications, especially under illumination variations. However, fewer statistical characteristics of the gradient phase have been reported till now. First, the statistical characteristics of the gradient phase against gradient signal-to-noise ratios (gradient SNRs) were investigated. The analysed results show that the confidence (or standard deviation) of gradient phases against gradient SNRs should never be linearly related, as is usually supposed. With the help of the statistical analyses of the gradient phase, the gradient-based visual detection and recognition were improved by incorporating confidence information into the cost function. Moreover, inspired by the analysed characteristics of the gradient phase, an enhanced gradientface method is proposed to improve the performance of the gradient phase-based face recognition. Intensive simulations and comparisons are performed to show its superior performance without the side effect of discrimination loss that existed in some illumination normalisation approaches.