Gender Classification using Facial Embeddings: A Novel Approach
Avinash Swaminathan, Mridul Chaba, Deepak Kumar Sharma, Yogesh Chaba
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%.