An improved hybrid illumination normalisation and feature extraction model for face recognition

A new illumination normalisation scheme based on reflectance ratio (RR) and contrast stretching (CS), feature extraction by integer wavelet transform (IWT) in fisher subspace for face recognition is proposed in this paper. RR is ratio of pixel intensity to average pixel intensity of its neighbourhood that discards illumination effects which is followed by CS to obtain further illuminated normalised images. Robust feature extraction is acquired by selecting low frequency coefficients and ignoring high frequency coefficients using IWT which makes proposed scheme computationally efficient. Illumination normalised feature extraction by combination of RR-CS and IWT model is followed by fisher linear discriminant analysis (FLDA) to achieve robust feature vector that provides best projection direction of training and test data sets. The recognition accuracy of 100% on CMU-PIE, Yale B and 99.05% (average) on extended Yale B face databases along with comparison with state of art methods proved strength of proposed model.