Methods for Human Identity Verification in Smart Unmanned Supermarkets Based on Computer Vision

Smart unmanned supermarkets have been emerging in the past years and significantly affected conventional shopping styles. Customer identification is an important step in picking up goods in smart unmanned supermarkets. However, in the actual application scenario, the camera can only capture part of the human body information, which is a challenge to identify individual consumers. This paper introduces an approach that integrates facial recognition with body re-identification (REID) algorithms to continuously update human feature information. The object detection method based on YOLOV4 is adopted to detect the human body in the unmanned supermarket. A full-scale network OSNet is used for human feature extraction. Finally, the cosine similarity of the extracted feature vector is calculated to obtain the similarity of human features. The experimental results show that the final similarity exceeds the set threshold value of 0.6, which can meet the needs of identity recognition for unmanned supermarkets.