Face recognition systems have become increasingly important in many applications including security, surveillance, access control, identity verification, and human-computer interaction. One of the primary objectives of a face recognition system is to identify individuals from their facial features. This can be used to authenticate the identity of an individual or to detect a known individual in a crowd. Another objective is to verify the identity of an individual based on their facial features. This can be used for access control or to confirm the identity of an individual for various purposes such as financial transactions, travel, and other secure applications. Face recognition systems can improve security by automatically identifying and flagging individuals who pose a threat. This can be useful in public spaces, airports, border control, and other high-security areas. Face recognition systems can also provide convenience in various applications such as unlocking a device or accessing a secure area without the need for physical keys or passwords. Accuracy of face recognition is affected by various challenges in input images such as occlusion, pose, aging, expression, low resolution, and illumination variation. This proposed work aims to examine and overcome the substantial difficulties in transferring current facial recognition algorithms to the real world. Here methodological approaches used for the proposed model are the viola-jones algorithm with haar cascade, grey level co-occurrence matrix, gabor filter, cross- validation, and support vector machine. After applying the above methods at various stages of the face recognition process; the proposed model provides a person’s identity with high accuracy on standard video datasets and many standard image datasets.

Computer vision, Face recognition, Face detection, Cross validation, Support vector machine