Soundless Credential Validation based on Lip Synchronization in Image Processing

In today's modern world, ensuring safe access to sensitive information and digital systems is essential. Traditional authentication techniques, such as passwords and PINs, are vulnerable to a wide variety of security concerns, including phishing attacks and password leaks. Silent Lip Password Recognition (SLPR) is an emerging biometric authentication method that relies on the visual analysis of an individual's lip movements during speech. This technique has garnered significant attention due to its non-intrusive nature and potential for enhancing security in various applications. Convolutional Neural Networks (CNNs) have proven to be highly effective in image-based tasks, making them a suitable choice for extracting meaningful features from lip motion data. This approach combines spatial and temporal information within the lip motion data, allowing the network to capture the nuances of individual lip patterns. The results show that CNNs can effectively distinguish and authenticate users with high precision, while also providing resistance to potential spoofing attempts