Fractional DCT and DWT hybridization based efficient feature extraction for gender classification

Abstract

Fractional discrete Cosine transform (FrDCT), generalisation of discrete Cosine transform (DCT), is a useful mathematical tool that provides flexibility to represent data at any angle α from spatial domain axis. Fusion of discrete wavelet transform (DWT) and FrDCT is used in this paper for gender classification. Initially, 1-level DWT is calculated for 128×128 resized input images. LL sub-band images are then input to FrDCT which transforms the images into u-domain which is at an angle α from spatial domain axis. FrDCT transformed images are used to generate a feature vector based on 8×8 non-overlapping sub-blocks. Dividing an image into sub-blocks helps to remove the local spatial correlation. Thus, proposed technique maintains the computational cost as that in other existing techniques. Rbf kernel based Support Vector Machine (SVM) is used to classify the images on the basis of gender. Images of AT&T, Faces94 and Georgia Tech databases are used to validate the performance of the proposed technique. It is found that proposed technique outperforms as compare to other existing techniques with respect to generalization performance.