GENDER RECOGNITION BASED ON SIFT FEATURES

This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consumingpre-processing step in order to alignment in which face images are aligned so that facial landmarks likeeyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based onmathematical analysis is represented in three stages that eliminates alignment step. First, a new colorbased face detection method is represented with a better result and more robustness in complexbackgrounds. Next, the features which are invariant to affine transformations are extracted from eachface using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposedalgorithm, experiments have been conducted by employing a SVM classifier on a database of face imageswhich contains 500 images from distinct people with equal ratio of male and female