A comparison of face verification with facial landmarks and deep features

Face verification is a key task in many application fields, such as security and surveillance. Several approaches and methodologies are currently used to try to determine if two faces belong to the same person. Among these, facial landmarks are very important in forensics, since the distance between some characteristic points of a face can be used as an objective measure in court during trials. However, the accuracy of the approaches based on facial landmarks in verifying whether a face belongs to a given person or not is often not quite good. Recently, deep learning approaches have been proposed to address the face verification problem, with very good results. In this paper, we compare the accuracy of facial landmarks and deep learning approaches in performing the face verification task. Our experiments, conducted on a real case scenario, show that the deep learning approach greatly outperforms in accuracy the facial landmarks approach.