Face Gender Image Classification Using Various Wavelet Transform and Support Vector Machine with various Kernels
When we look at a face, we readily identify that persons gender (male/female), expression (happy /unhappy), personality (He / She), age, and charisma. Gender classification such as classifying human face is only challenging for computer, but even hard for human in some cases. In this paper a new novel approach is proposed to recognize gender from the face image. Continuous Wavelet Transforms are used for features selections for each face images of male and female. These selected features will be used to classify the face images of each Gender using Support Vector Machine with Linear Kernel. This Paper use ORL database contain 400 images include both Male and Female Gender .The experimental result shows that the proposed approach (Continuous wavelet Transform and Support Vector Machine) achieves excellent classification accuracy (98% )compared with other Technique like Discrete wavelet Transform and Radon Transform with Support Vector Machine.
Keywords: Face Gender Classification, Feature Selection, Continuous Wavelet Transform (CWT), Discrete Wavelet Transform, Radon Transform. Support Vector Machine (SVM)
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ABOUT THE AUTHORS
Amjath Fareeth Basha
Amjath Fareeth Basha, Lecturer, College of Computers and Information Technology, TAIF UNIVERSITY, Kingdom of Saudi Arabia.
Gul Shaira Banu Jahangeer
Gul Shaira Banu Jahangeer, Lecturer, College of Computers and Information Technology, TAIF UNIVERSITY, Kingdom of Saudi Arabia.
Amjath Fareeth Basha
Amjath Fareeth Basha, Lecturer, College of Computers and Information Technology, TAIF UNIVERSITY, Kingdom of Saudi Arabia.
Gul Shaira Banu Jahangeer
Gul Shaira Banu Jahangeer, Lecturer, College of Computers and Information Technology, TAIF UNIVERSITY, Kingdom of Saudi Arabia.
