. Novel Gender And Age- Based Detection Technique for Facial Recognition System

This paper introduces gender and age-based classification approaches in facial recognition systems, addressing challenges posed by diverse demographic characteristics.The model learns typical facial features and identifies deviations from them by employing unsupervised detection methods using autoencoders, enhancing robustness and generalization across populations.Ethical considerations are discussed, emphasizing the importance of fairness and bias mitigation in facial recognition.Experimental results demonstrate the method's effectiveness in handling biases in traditional supervised approaches.This research contributes a novel technique while highlighting the ethical implications of facial recognition.Gender and age-based detection methods improve system reliability in real-world scenarios with diverse demographics.These findings are relevant to researchers, developers, and policymakers navigating the intersection between facial recognition and ethical AI, promoting more responsible and inclusive technology.