Face and face key point joint detection method based on transfer learning

Abstract

The invention discloses a rapid face and face key point joint detection method, comprising the following steps: 1, constructing a teacher network and a student network; 2, inputting a batch of training images, and carrying out data enhancement; 3, dividing positive and negative anchor frame samples according to a self-adaptive scale matching strategy; 4, mining positive and negative samples, calculating a multi-task loss function, and updating network parameters; 5, turning to the step 2 until the training converges to obtain a teacher network model; 6, repeating the steps 2 to 5, adding a transfer learning loss function by using the teacher network model, and training to obtain a student network model; and 7, in a test stage, inputting a test image to the student network model to obtain adetection result. The rapid face and face key point joint detection method can obtain face and key point detection results at the same time, and can increase the speed of the face recognition preprocessing process. The lightweight network provided by the invention is high in reasoning speed and can be deployed in embedded equipment with limited computing power.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and specifically relates to a method for joint detection of human faces and key points of human faces based on transfer learning, which can be applied to many fields such as video monitoring, identity recognition, and human-computer interaction. Background technique [0002] Face detection is a technology that automatically searches for the position and size of a face in any image. Face key point detection is a technology that automatically searches for defined face feature points (such as pupils, nose, corners of mouth, etc.) in any image. ) position technology. Face detection and face key point detection play an important role in computer vision and pattern recognition applications, such as video surveillance and access control systems. For face recognition, face detection and face key point detection are two essential preprocessing steps. [0003] At present,...

AI Technical Summary

Problems solved by technology

However, as the scale of the face becomes smaller, the detection performance of MTCNN will drop sharply.

With the increase of faces in the image, the detection time of MTCNN will increase sharply

In general, the challenges of designing embedded face detection and face key point detection networks mainly include the following three aspects: First, there is a lot of redundancy in network parameters in traditional detection methods, which is not in line with embedded The main structure of the embedded network should be short and lean to meet the power consumption requirements of the equipment, so as to ensure the computing power and inference speed of the network

Secondly, the matching strategy of the traditional anchor frame and the face annotation frame is not perfect. Face annotation frames of some scales cannot match enough anchor frames, resulting in their low recall rate and the covered face scale range. not wide enough

Finally, multi-task learning under the lightweight network framework will often bring a certain loss of accuracy, because the parameter redundancy of the lightweight network is small, and the network capacity is not large enough.

Images