GENDER RECOGNITION AND AGE DETECTION USING HUMAN FACIAL FEATURES

Abstract

One of the active study fields in recent years is the extraction of information from the human face. Numerous investigations on the identification of the most prevalent face variety, including age and gender, have been done. The approach for automatically determining a person's age and gender from their face is suggested in this paper and is based on convolutional neural networks and support vector machines. The four steps of this method are face detection, pre-processing, feature extraction, and classification. A variety of convolutional neural networks have been trained to detect faces in live feed and also determine their age and gender are part of our system. Our model was trained using Adience benchmark dataset for age and gender prediction available on Kaggle, which has produced positive results. Keywords: Convolution neural network, Support Vector Machine, Face Detection, Multi-Task Cascaded Convolution Neural Networks, Principal Component Analysis, Adience benchmark dataset.