Gender Classification using Facial Embeddings: A Novel Approach

Abstract

Image Processing for Human recognition involves using bio-metric traits such as Face, Iris, Voice and other physical traits to uniquely identify human faces. With the increase in Image Data on the Internet, there is a huge demand for Artificial Intelli-gence(AI) algorithms that can perform classification tasks like Race and Gender Classification. The advent of Deep Learning Techniques like Convolutional Networks has led to a rapid ascent in accuracy in various image classification tasks. Through this paper, a novel method to predict Gender of a person by applying various Machine Learning Classification Techniques on Facial Em-beddings has been proposed. The facial embeddings are found by passing through a Pretrained Inception Network. The maximum accuracy obtained by the proposed work to classify gender is 97%.

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Recommended citation: Avinash Swaminathan , Mridul Chaba, Deepak Kumar Sharma,and Yogesh Chaba. Gender Classification using Facial Embeddings: A Novel Approach. Procedia Computer Science , 167:2634 – 2642, 2020. International Conference on Computational Intelligence and Data Science.