Authors - Ashita C. Kolla, Dattatray G. Takale, Parikshit N. Mahalle, Bipin Sule, Gopal Deshmukh Abstract - The research paper mainly focuses on algorithmic bias in facial recognition technology using parameters like race and hairstyle. It involves a CNN model following the pre-processing step of the data and custom annotation. It further talks about advanced methods for dataset balancing, such as normalization and sampling, along with detailed annotations involving characteristics of different races and hairstyles. Compared to other models, the CNN model contains powerful feature extraction methods and other bias mitigation methods such as adversarial training and annotation to enhance the chance of predictions. The results reveal that the model has made significant progress with good performance and lesser bias. This study helps the industry develop more reliable FRT systems with effective strategies for reducing bias and maintaining accuracy. These advancements are important for applications in various industries, where unbiased facial recognition is important for fairness and effectiveness.
