Gender Classification Using Machine Learning with Multi-Feature Method

Nowadays gender classification is a very challenging task in a real-time application based on face recognition. The demand for real-time application based on gender classification will increase in the future. Bag of Words (Bow), Scale Invariant Fourier Transform (SIFT) and K-means clustering are used for feature extractors and classification. This state of art methodology gives more efficient result on different standard datasets. This research proposed a new algorithm for automatic live Gender Recognition (GR) using Support Vector Machine (SVM) is used for classification. The implementation of result work tested on FEI, Live Images and SCIEN database for GR. The detection rate has reached up to 98% in FEI dataset, 94% in Live/Own dataset and 91% SCIEN dataset respectively. This proposed state of art methodology is compared with the previous techniques and achieved better results which will help in the development of real-time identification systems.