Abstract:
Many developments have taken place in the field of face-recognition and liveness analysis to improvise various device securities and attendance verification systems. Many approaches have incorporated 3D analysis of the face to predict the liveness of the person in front of it. Our method tries to account for this problem without using advanced 3D imaging techniques or hardware. This results in a solution that is both, more economical and also much easier to deploy. It consists of two parts; the former helps in face verification and the latter to check the liveness of the face. In the first part, we have used a model based on Google’s FaceNet Model which learns a mapping from face images to compact Euclidean space distances, which directly correspond to the measure of similarity of the images. Once the space has been produced, face verification can be easily implemented using standard techniques with embeddings as feature vectors. For the second part, we have employed a cascaded multi-task framework that extracts certain features from the facial image which are then used to check for liveness by tracking their relative displacements. These extracted features were used to check the liveness of the person’s face by asking them to perform some tasks in a random order like head and facial movements etc.
