A FACE RECOGNITION SYSTEM USING MULTI-TASK CASCADED CONVOLUTIONAL NETWORKS AND FACENET MODEL

Abstract

The convolutional neural networks (CNN) is one of the most successful deep
learning model in the field of face recognition, the different image regions are always
treated equally when extracting image features, but in fact different parts of the face
play different roles in face recognition. In this paper, we use the inherent correlation
between detection and calibration to enhance their performance in a deep multitask cascaded convolutional neural network (MTCNN). In addition, we utilize
Google’s FaceNet framework to learn a mapping from face images to a compact
Euclidean space, where distances directly correspond to a measure of face similarity
to extract the performance of facial feature algorithms. The weighted average
pooling algorithm is applied to the FaceNet network, and a face recognition
algorithm based on the improved FaceNet model is proposed. The experiments and
apply system show that the proposed face recognition algorithm has high
recognition accuracy using face recognition method based on deep learning.