Age and Gender Recognition using Deep Learning Technique

Abstract

Gender classification is popular because it includes information about male and female social activities. Faces make it difficult to derive gender-discriminating visuals. Gender classification is based on looks. Automatic gender classification is popular because genders include rich social information. Classification has grown increasingly important in many industries. In a conservative society, gender classification can be usedin certain contexts. Identifying gender type is crucial to keeping extremists out of safe locations, especially in sensitive areas. A similar technique is utilized in female-only railway carriages, gender-specific marketing, and temples. Biometrics debates gender classification from facial pictures. Traditional ways categorize hand-crafted features globally and locally. These gender-identification systems need subject knowledge and are ineffective. Human gender identification is easy, but machines struggle. We listed numerous gender classification pre-processing approaches, such as contrast and brightness normalization. To create a gender and age classification framework Deep Belief Networks employs Shifted Filter Responses to identify features. The suggested model achieves 98% and 99% accuracy on the benchmark dataset.