Abstract
A contactless delivery cabinet is an important courier self-pickup device, for the reason that COVID-19 can be transmitted by human contact. During the pandemic period of COVID-19, wearing a mask to take delivery is a common application scenario, which makes the study of masked face recognition algorithm greatly significant. A masked face recognition algorithm based on attention mechanism is proposed in this paper in order to improve the recognition rate of masked face images. First, the masked face image is separated by the local constrained dictionary learning method, and the face image part is separated. Then, the dilated convolution is used to reduce the resolution reduction in the subsampling process. Finally, according to the important feature information of the face image, the attention mechanism neural network is used to reduce the information loss in the subsampling process and improve the face recognition rate. In the experimental part, the RMFRD and SMFRD databases of Wuhan University were selected to compare the recognition rate. The experimental results show that the proposed algorithm has a better recognition rate.
Highlights
Face recognition with occlusion has attracted extensive attention in academic circles
Mathematical Problems in Engineering people’s dynamic behaviors and actions and maintain pixel tracking allocation even under large shielding. It has been used in different experiments of indoor human behavior supervision due to its robustness. e methods for processing occluded images can be divided into five categories: low-rank representation, image restoration, fuzzy analysis, robust principal component analysis, and structural occlusion coding
Literature [12] proposed the method of sparse error and graphical model to continuously overlap and display the mask. e Markov random field model is transformed into the calculation of the sparse representation of the training image, so as to find the occlusion area accurately. erefore, great research significance lies in how to separate the blocked face images from blocking images
Summary
Face recognition with occlusion has attracted extensive attention in academic circles. Literature [6] proposed a robust low-rank representation method to solve the problem of face recognition with occlusion. Low-rank robust principal component analysis is a mainstream occlusion face feature extraction method, which combines structural occlusion coding with sparse representation classification. Literature [9] proposed a new nonnegative sparse representation method for robust face recognition in large-scale databases This algorithm has a large amount of computation and a complex structure. Literature [10] proposed an occlusion dictionary method, which plays an increasingly important role in face recognition and can effectively deal with various occlusion objects It can distinguish the features of nonoccluded and occluded regions and encode the corresponding parts of the dictionary, respectively. According to the important feature information of the face image, the attention mechanism neural network algorithm is used for face recognition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
