Deep Face Age Net: A Real-Time Multi-Task Gender and Age Detection Framework Using Deep Learning and Computer Vision

Abstract

In recent years, deep learning and computer vision have made substantial progress, which has led to the profound changes of intelligent facial analysis systems. Intelligent facial analysis systems have undergone significant transformation over the last few years due to the quick development of deep learning and computer vision technologies. The detection of gender and age have been gaining relevance in various applications such as surveillance, health, smart retail, human-computer interaction, and security. This research proposes a real-time gender and age detection system using deep learning and convolutional neural network (CNN). The system that is proposed combines face detection using OpenCV techniques with the use of pre-trained CNN models to provide gender classification and to predict age groups from facial images and live video streams from a webcam. The framework employs benchmark datasets like UTKFace and IMDB-WIKI for enhancing the reliability and robustness of the predictions. The face localization is achieved by using the Detector based on Deep Neural Network (DNN). The proposed system also includes pre-processing and feature extraction steps to classify the gender and age of the face. The results of the experiments prove that the proposed system is capable of achieving high accuracy in gender classification, good performance with age group estimation and still able to process the input data in real-time. The system is able to deliver efficient prediction results for different lightings and facial orientations. The proposed solution, which is lightweight, scalable, and practical, is a valuable addition to the field of intelligent vision systems, providing a lightweight, scalable and practical solution for real-time demographic analysis.