Face Detection and Recognition Based on Visual Attention Mechanism Guidance Model in Unrestricted Posture

Zhenguo Yuan

doi:10.1155/2020/8861987

Abstract

Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face; the face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.

Highlights

Erefore, it is of great practical significance to study the occlusion problem for face detection and recognition task [4]
Aiming at the problem that occlusion affects the accuracy of face detection and recognition, this paper proposes a deep network with multilevel feature fusion. is network uses a visual attention mechanism to guide the model to highlight the visible area of the occlusion face; the detection recognition problem can be simplified to a highlevel semantic feature detection problem, and the position and scale of the face are predicted by means of activation maps, avoiding additional parameter settings
A large number of simulation experiment results show that the proposed method is better than the existing mainstream method in the detection and recognition of the occlusion face on the public data set and has achieved a faster detection speed, which can be used in the field of security surveillance

Summary

Introduction

Erefore, it is of great practical significance to study the occlusion problem for face detection and recognition task [4]. Wang et al [8] used 3-dimensional face information as a feature where the robustness and accuracy of the algorithm are improved through a large amount of data training. In order to solve this kind of problem, Su et al [12] proposed a multiinception structure-based convolutional network neural algorithm for face recognition. Aiming at the problem that occlusion affects the accuracy of face detection and recognition, this paper proposes a deep network with multilevel feature fusion. A large number of simulation experiment results show that the proposed method is better than the existing mainstream method in the detection and recognition of the occlusion face on the public data set and has achieved a faster detection speed, which can be used in the field of security surveillance

Methods

Results

Conclusion