RETRACTED ARTICLE: Fraud detection and prevention by face recognition with and without mask for banking application

It gives an innovative strategy for detecting and preventing fraud in banking applications, with a specific focus on incorporating face recognition technology. In this proposed methodology, a Seagull-based convolutional neural network (SbCNN) is utilized to tackle crucial challenges related to facial recognition, especially in scenarios where individuals are wearing masks. The primary objective is to elevate security measures by proficiently identifying and verifying individuals regardless of whether they are wearing masks or not. Leveraging the sophisticated capabilities of the CNN ensures precise and robust processing of facial features. A key concern addressed is the prevalent issue of individuals using masks, a challenge that has become a focal point in security applications. The proposed method actively acknowledges and aims to provide a comprehensive solution to mitigate potential risks associated with fraudulent activities within the banking sector. It underscores the significance of adapting security protocols to contemporary challenges, positioning the Seagull-based CNN as a proactive and effective approach to fortify the security of banking applications in response to evolving threats.