Study on Face Recognition Scheme for Improving the Way of Material Handover

Abstract

Aiming at the current status of traditional material handover management in factory production, an improvement plan based on face recognition is proposed, which simplifies the handover process and improves the handover efficiency. When employees hand over materials, they collect face images through terminal equipment and upload them to the server for face detection and recognition. Because the face detection and recognition in the factory environment is susceptible to low light, backlight, semi-shading and other light conditions, this solution proposes an image preprocessing technology, which can effectively improve the accuracy of face detection and recognition by adaptively enhancing the light of the picture. At the same time, MTCNN algorithm is used for face detection. Through performance testing and analysis, it is verified that the algorithm has obvious advantages in computing speed and detection accuracy. Face recognition uses the face recognition model of Dlib library to extract the feature value of the detected face, and compare it with the face information database to obtain the recognition result. A set of face recognition schemes are implemented for material handover, which is effective Improve the efficiency of material transfer in factory production management.