Face Verification with Liveness detection using Deep Learning
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2022-12-31
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Numerous advancements in the area of face detection and liveness analysis have been made to improve device security and attendance verification systems. Several methods use the 3D facial model to estimate the authenticity of the individual in front of it. Without using complex 3D imaging techniques or technology, our solution attempts to account for this difficulty. As a result, the system is indeed more cost-effective and convenient. It is divided into two sections, the first of which aids in face recognition and the second of which checks the liveness of the face. We employed a model based on Google's FaceNet Model in the first stage that trains a mapping from face images to compact Euclidean space distances, specifically relating to the similarity measure between the faces. Face Recognition may be simply accomplished using normal approaches using embeddings as feature vectors after the space has been created. We built a cascaded multi-task architecture for the second segment that separates specific elements from the face picture and then uses their relative displacements to verify for liveness. These separated characteristics were utilized to test the liveliness of a person's face by having them do a series of activities in a random order, such as body and facial twitches. The FaceNet based face detection model has an accuracy of 90%, and the facial features extraction model has 97% accuracy. After merging both models in real-time, we have an accuracy of 90%
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Electrical and Computer Engineering
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North South University