Real-time age-range recognition and gender identification system through facial recognition

Abstract

Facial recognition and age estimation are being implemented in apparel retailing which is undergoing significant changes due to fashion and technology. To improve interaction with customers and refine marketing strategies. The paper proposes an approach based on a Siamese neural network and the use of tools such as MediaPipe for face detection and DeepFace for age and gender estimation. In addition, the four stages of the research work, real-time image capture, ID assignment, facial feature extraction, and data storage, are described. Early approaches to age estimation were based on biometric features, such as eyes, nose, mouth, and chin, resulting in limited accuracy and low performance in older adults. To improve accuracy, additional elements, such as the presence of wrinkles, were considered and a diverse database of images was used. The proposed methodology achieves a positive result for real-time age classification and gender ID. The results include information on gender, age, ID, time and date for each person identified.