Deblurring Method of Face Recognition AI Technology Based on Deep Learning. (8th April 2022)

• Record Type: Journal Article
• Authors: Li, Weilong; Li, Jie; Zhou, Junhui
• Other Names: Li Qiangyi Academic Editor.
• Is Part Of: Advances in multimedia. Volume 2022(2022)
• Publication Date: 2022-04-08
• Subjects: Multimedia systems -- Periodicals; Computer networks -- Periodicals; Multimédia; Réseaux d'ordinateurs; Computer networks; Multimedia systems; Periodicals; 006.7
• DOI: 10.1155/2022/9146711
• Languages: English
• ISSNs: 1687-5680

Abstract

Abstract : As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. The features extracted by traditional face recognition methods are greatly influenced by subjective factors and are time-consuming and laborious. In addition, these images are susceptible to illumination, expression, occlusion, posture, and other factors, which bring a lot of interference to the computer face recognition and increase recognition difficulty. Deep learning is the most important technical means in the field of computer vision. The participation of this technology reduces manual participation and can identify the identity of visitors from multiple aspects. This study, based on the introduction at all levels and on the fundamental principle of the colloidal neural network, combines the basic model and the common exploitation methods of aspects to make a model of a combination of multiple aspects. Then, an improved CNN-based multifeature fusion face recognition model is proposed, and the effectiveness of the model in face feature extraction is verified by experiments. With the experimental results, the identification rate for the ORL and Yale data sets is 98.2% and 98.8%, respectively. Accordingly, an online face detection system and recognition system based on the combination of element models are designed. The system can obtain dynamic facial recognition and meet the recognition rate of the design requirements. The system is training four detection models and online recognition, and the test results show that the noise component model has the highest recognition rate, and the recognition rate has improved by 13% compared with the baseline capacity, further verifying that a model of a combination of features can achieve more effectively.