A study on the CNN-based face recognition across various facial angles

Abstract

Ace recognition is a prominent computer technology with a broad range of applications, such as identity verification and face payment. Over the years, significant advancements have been made in the research and practical implementation of face recognition in academia. However, the variability of face images in real-world scenarios poses challenges to accurate face recognition. Specifically, the variation in facial angles affects the extraction of crucial face information by the model, yet there is a lack of research focusing on the angle factor's impact on face recognition. In this study, we collect and process face datasets, construct a convolutional neural network (CNN) model, capture images containing faces via a camera, detect and track faces from it automatically, and then recognize the detected faces. We investigate the accuracy of face detection and recognition under different angles. Experimental results demonstrate that when the angle ranges from 0 to 30, face detection and recognition achieve excellent accuracy. Within the angle range of 30 to 45, the performance remains within an acceptable range. However, when the angle exceeds 45 and reaches 60, face detection and recognition accuracy decline significantly, resulting in poor performance.