Wavelet Based Face Recognition for Low Quality Images
|M.Karthika1, K.Shanmugapriya2, Dr.S.Valarmathy3, M.Arunkumar4
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The appearance of a face image is severely affected by illumination conditions that hinder the automatic face recognition process. Face recognition (FR) under invariant conditions is challenging, and exacting illumination invariant features is an effective approach to solve this problem. In this work, a multi-resolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. Wavelet coefficients corresponding to each local region residing inside those horizontal bands are selected as features. In the selection of the dominant coefficients, a threshold criterion is proposed, which drastically reduces the feature dimension . The contribution of this paper is threefold: 1) an objective measure of illumination quality of a given face image is used to decide if the image should be pre processed to normalize its illumination; 2) the global quality-based normalization scheme is extended to a regional quality-based approach to adaptive illumination normalization; 3) the illumination quality measure is used as a means to adaptively select the weighting parameters of the fused wavelet-based multi stream face recognition scheme.