DESIGN AND IMPLEMENTATION OF A GENDER BASED FACIAL RECOGNITION SYSTEM.

A gender-based facial recognition system is an innovative technology that can accurately identify and classify individuals based on their gender. The existing facial recognition systems, which often struggle with accurate gender classification, particularly for individuals with diverse gender identities, pose a significant problem. This project provides a solution by designing and implementing a facial recognition system that accurately identifies and classifies individuals based on their gender. Hence, this project aims to design and implement a gender-based facial recognition system. The methodology employed in this project involves a comprehensive literature review of existing facial recognition systems, followed by the collection and pre-processing of a diverse dataset of facial images. The project utilizes a deep learning approach, specifically a convolutional neural network (CNN), to develop the facial recognition model. The model is trained and tested using the collected dataset. The implementation and testing of the gender-based facial recognition system involve evaluating its performance using 3 sample datasets, each with 10 test cases. Metrics such as True Positives, False Negatives, False Positives, and True Negatives are used to calculate accuracy, False Acceptance Rate, and False Rejection Rate. The system achieves over 90% accuracy across all samples, demonstrating its effectiveness and reliability. In conclusion, this project contributes to the development of a more accurate and inclusive facial recognition system. The proposed system has the potential to improve the accuracy of facial recognition for individuals with diverse gender identities. The findings of this project can be applied to various real-world applications, such as security, law enforcement, and social media.