Face Recognition of Remote Teaching Video Image Based on Improved Frame Difference Method

Remote network teaching has gained significant importance in recent times, with video images serving as a crucial medium for delivering educational content. Ensuring accurate face recognition in these video images is a key challenge. To address this, we present a face recognition algorithm based on an improved frame difference method. The algorithm focuses on enhancing the accuracy of face recognition specifically in remote network teaching video images. By leveraging a generative adversarial network method, we enhance image resolution as a preprocessing step. Subsequently, our proposed image target detection algorithm effectively identifies the face region through foreground and background segmentation. We employ an improved local three-value pattern for face feature extraction, concentrating on the face target region. These features are then input into an integrated neural network face recognition model. Experimental results demonstrate the algorithm's efficacy in enhancing clarity processing, facial object detection, and feature extraction for remote teaching video images. Notably, the proposed method achieves an average gradient of details below 0.1 and attains a facial feature matching degree of 0.98, establishing the high accuracy of facial recognition results in remote teaching video images.