The Application of Face Recognition Model Based on MLBP-HOG-G Algorithm in Smart Classroom

The development of Internet and Internet of things technology has accelerated the informatization construction of smart education. But the traditional face recognition algorithm used in smart classrooms inevitably has problems such as large amount of calculation, obvious resource and memory consumption, and poor recognition accuracy. In order to promote the informatization construction of colleges and universities and the accuracy of face recognition, a face recognition model based on multi-feature Local Binary Pattern Directional Gradient Histogram Gabor Filter algorithm is proposed. The model first extracts the binary texture image, and then carries out secondary feature extraction, dimension reduction processing and serial fusion with the gray level co-occurrence matrix feature weighting to improve the recognition accuracy. The results show that the recognition rate of the proposed method in ORL database, CMU_PIE database and Yale database can reach 95%, 94.12% and 93.33%, which is better than other algorithms. And in the comprehensive data set, the training and verification recognition accuracy of the proposed method for face recognition is basically 98% and 97.23%, which has good generalization and stability, and its cumulative error result of face key point detection is less than that of other comparison methods. The proposed method can provide new opportunities and possibilities for the application effect of face recognition, smart classroom construction and teaching development.