Classification of child and adulthood using GLCM based on diagonal LBP

This paper derives a child and adulthood classification technique by integrating the statistical and structural approaches. The structural approaches are derived on a 3 × 3 window based on Local binary pattern (LBP) approach. The proposed approach divides the LBP in to two structural patterns. The present paper derives two distinct patterns called Left Diagonal (LD) and Right Diagonal (RD) LBP´s. The given image is converted into binary by comparing the average value of the 3 × 3 neighborhood with its neighbors. Then LD-LBP and RD-LBP codes are evaluated. The range of these code values will be 0 to 23-1, since three pixels form the above proposed patterns. Based on LD and RD-LBP the present paper derived left and right diagonal-GLCM (LRD-GLCM) and features are evaluated. For efficient age classification chi-square distance method is used. To overcome the data dependency problem, the proposed method is implemented on three different facial namely FG-NET, Google and scanned images. The experimental results indicate a significant age classification rate over the existing methods.