Different lighting processing and feature extraction methods for efficient face recognition

This study studies different lighting processing and feature extraction methods for efficient face recognition. The purpose is to find some robust face recognition methods by combining different feature extraction methods with illumination compensation. In this study, some typical illumination preprocessing approaches are reviewed including wavelet transformation, self-quotient image, Retinex, smoothing, discrete cosine transform normalisation in logarithm domain, homomorphic filter and local contrast enhancement. As the main contribution, this study proposes two efficient feature extraction methods for face recognition. One is an adaptive feature extraction (AFE) based on curvelet transform. The other is a feature extraction technique named two-dimensional principal component analysis (2DPCA) non-parametric analysis of 2D subspace (2DPCA + 2DNSA). Two groups of experiments are designed to verify the proposed methods. The first group of experimental results show that the proposed AFE methods have better performance than conventional methods. In the second group of experiments, each feature extraction method is combined with nine different lighting processing methods. The results show that the proposed 2DPCA + 2DNSA method is more robust to lighting processing than other methods. Experimental results also show that lighting processing contribution to face recognition are quite different for different face databases.