Gender Recognition from Selfie Images by Merging Convolutional Neural Networks with Genetic Algorithms

Abstract

Human gender plays an imperative role as social construct and an essential form of an individual’s personality. Gender recognition is a fundamental task for human beings. It is highly reflected in social communication, forensic science, surveillance, and target marketing. Gender recognition previously depended only on standard face images. Using the term “standard” means that the image was taken in a standard light without any background variations and without any cropped parts. But this type of image is not found in real-world. These images are called non-standard images, as they have a lot of variations, like illumination and head pose. The image may also have a lot of faces, where one of them may wear sunglasses or other accessories. Using this type of image will affect the accuracy results of gender recognition approaches. Nowadays, selfie images appear as they are unconstrained images. People take selfie images of themselves. Selfie images are very complex, as some parts of the images are cropped and damaged. This paper proposes a (CNNGA) technique for gender recognition from selfie images by merging a deep learning approach with genetic algorithms. The proposed technique achieves 90.2% accuracy in recognizing gender from the selfie dataset. The experiments use various challenge datasets, which are widely adopted in the scientific community like LFW, Data Hub, FERET, and Caltech-web Faces.