Utilizing multi-task cascaded convolutional networks and Resnet-50 for face identification tasks

In recent years, computer-animated characters have been designed more and more vivid and lifelike, and many of them are extremely similar to real people. Due to the high degree of similarity, some classical face recognition models are mixed up in the face identification process. For example, FaceNet model matches cartoon facial images with their similar real faces. In this case, some people may try to cheat by using virtual faces when they are identified by face recognition systems. To address this problem, this paper proposes an integrated approach that utilizes Multi-task Cascaded Convolutional Networks (MTCNN) and Resnet-50 models for the classification of real and cartoon faces (or virtual faces) of an input image before the face identification task. Our experiments show that the proposed integrated approach achieves better results on face identification tasks compared to some classical face recognition models that accomplish the tasks directly.