Abstract
The existing face detection methods usually had the problem of low accuracy of face recognition in the environment of occlusion interference, which was limited when applied to the face detection task in complex scenes. Therefore, in order to realize high precision and real-time local face recognition in a complex environment, a face local attribute detection method based on improved SSD network structure was proposed. Based on the analysis of the face local attribute detection task, SSD was used as the basic detection network structure, and the VGG16 feature extraction model was used as the framework of face local detection. On this basis, by organically connecting different layers of the SSD network and integrating convolution block attention module, the improved SSD network structure was used to realize face local attribute detection. The proposed model was trained and tested using typical public datasets such as Wider Face, MAFA, and COFW. Experimental results showed that this method had high recognition accuracy, can better detect local features of the human face than other models, and can provide some support for local face attribute detection. This method would provide a theoretical basis and technical support for local face attribute detection in complex scenes.
Highlights
With the rapid development of the smart city and artificial intelligence technology, face detection and recognition play an important role in the field of digital intelligence [1, 2]
The face tracking method is very similar to the face recognition algorithm to a certain extent, there are often differences due to different application emphases. e research on face tracking mainly belongs to a direction in the field of computer vision, which is mainly applied to face monitoring and face recognition. e difficulty mainly focuses on the extraction of face features
E model in this article is trained on MAFA and COFW training sets. e results on the MAFA test set are compared with the experimental results of mainstream algorithms such as YOLOV2, DSSD321, and Faster R-CNN, as shown in Figures 7 and 8
Summary
With the rapid development of the smart city and artificial intelligence technology, face detection and recognition play an important role in the field of digital intelligence [1, 2]. Research on face detection and recognition has been developed with the indepth application of face tracking technology. E research on face tracking mainly belongs to a direction in the field of computer vision, which is mainly applied to face monitoring and face recognition. E research on face detection is to extract and process the features related to the face from the collected images and analyze the recognized face features. With the continuous application of deep convolution neural network in image and feature processing, how to apply deep convolution network to face image detection and recognition has attracted extensive attention
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