REINFORCEMENT SUPERVISED AGE AND GENDER DETECTION SYSTEM USING DEEP LEARNING
Abstract
Age and gender recognition has become an essential component in biometric systems, security, and personalized services. It is widely used for access control, targeted advertisement, and demographic analysis. In this project, a deep learning model was developed to accurately detect age and gender from facial images using convolutional neural networks (CNNs). The system is trained on a large dataset containing diverse facial samples, improving its prediction accuracy. Experimental results demonstrate the model’s ability to classify individuals’ age groups and gender effectively, offering reliable performance in real-time applications.the systemachieved high performance with accuracy of 94.6%, recall of 92.8%, and F1-score of 93.5%, showing improved adaptability and precision compared to conventional CNN models.
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