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In this project, gender classification is performed based on front fa?ade photos of 100 male and 100 female. In order to demonstrate the internal face images are aligned and cropped..
Even though some images are cropped about ears and hairs with the expense of the information loss about gender information at those parts, the main aim is achieving gender classification on internal face of the human body.
It? include 3 statistical values (average, standard deviation and entropy) and 4 parameters of GLCM (Gray Level Co-occurrence Matrix). 60% gender classification accuracy rate is achieved based on the generated frontal face image data set .
As a secondary method, features are extracted by means of GLCM method, followed by application of 2D DWT (The Discrete wavelet transform) technique on the original images. it has been established attribute of original images by respectively DWT (The Discrete wavelet transform) and GLCM (Gray Level Co-occurrence Matrix).?