Gender recognition using facial images

The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.

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Rubia Fatima recibió su título de Máster en Tecnología de la Información (TI) de la Universidad Bahauddin Zakariya (B.Z.U), Multan, Pakistán, en 2016. En la actualidad, está cursando un doctorado en Ingeniería de Software en la Escuela de Software de la Universidad de Tsinghua, en la República Popular China. Su investigación se especializa en ciberseguridad y educación basada en juegos.