Soft Biometrics Estimation Using Shearlet and Waveatom Transforms With Three Different Classifiers
Ahmad Fouad El-Samak, Mohammed A. Alhanjouri · 2019
The goal is to find the best feature extraction, which performs the smallest feature vector length and gives the highest performance. In this paper, we proposed a methodology to extract effective features from facial images using two multiresolution transforms; waveatom and shearlet, for estimating gender, ethnicity, facial expression and age. Three classifiers used to perform the final estimation, which are: Artificial Neural Network (ANN), Support vector machine (SVM) and Self-Organization Map (SOM). A comparative study is made to determine the best extractor and classifier. Experiments carried out on a large database collected from three different databases: US Adult Faces, Extended Cohn-Kanade and FG-NET database. The experimental results of the proposed methodology using waveatom transform proved to be effective in the three classifiers, In contrast of shearlet transform.