An Integrated Approach for Real-Time Gender and Age Classification in Video Inputs Using FaceNet and Deep Learning Technique

Abstract

The increasing demand for real-time gender and age classification in video inputs has spurred advancements in computer vision techniques. This research work presents a comprehensive pipeline for addressing this challenge, encompassing three pivotal tasks: face detection, gender classification, and age estimation. FaceNet effectively identifies faces within video streams, serving as the foundation for subsequent analyses. Moving forward, gender classification is achieved by utilizing a finely tuned ResNet34 model. The model is trained as a binary classifier for the gender identification. The optimization process employs a binary cross-entropy loss function facilitated by the ADAM optimizer with a learning rate of 1e-2. The achieved accuracy of 97% on the test dataset demonstrates the model's proficiency. The ADAM optimizer with a learning rate 1e-3 is used to train with the Mean Absolute Error (MAE) loss function. The evaluation metric, MAE, underscores the model's effectiveness, with an achieved MAE error of 6.8, signifying its proficiency in age estimation. The comprehensive pipeline proposed in this research showcases the individual components' efficacy and demonstrates the synergy achieved through their integration. Experimental results substantiate the pipeline's capacity for real-time gender and age classification within video inputs, thus opening avenues for applications spanning diverse domains.