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|Title||Soft Biometrics Estimation Using Shearlet and Waveatom Transforms With Three Different Classifiers|
|Title in Arabic||تقدير السمات الحيوية الناعمة باستخدام تحويل Shearlet و Waveatom مع ثلاثة مصنفات مختلفة|
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.
|Published in||2019 IEEE 7th Palestinian International Conference on Electrical and Computer Engineering (PICECE)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
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