Super-resolution reconstruction of biometric features recognition based on manifold learning and deep residual network. (June 2022)

• Record Type: Journal Article
• Authors: Ge, Huilin; Zhu, Zhiyu; Dai, Yuewei; Liu, Runbang
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Super-resolution reconstruction -- Deep learning -- Manifold learning -- Facial expression recognition -- ResNet50 -- Weight feature; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106822
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Acquired face information has practical application value. Image information of the face is influenced by noises. This research reconstructs low-resolution facial expression images. Simplified residual block network and manifold learning are implemented. A joint supervision through a new hybrid loss function is proposed. Abstract: Background and objective: In daily life, face information has the characteristics of uniqueness and universality. However, in a real-world scene, the image information of the face acquired by the acquisition device often contains noises such as blurring and sharpening. As such, super-resolution reconstruction of face features recognition based on manifold learning is proposed in this paper. Methods: We reconstruct low-resolution facial expression images, introduce a simplified residual block network and manifold learning, and propose joint supervision through a new hybrid loss function, which not only retains the color and characteristics of the image, but also retains the high-frequency information. The ResNet50 network uses the weight feature of information entropy to optimize the information of the pooling layer, and the esNet50 network uses the improved PSO algorithm to optimize the initial weight of the error back-propagation phase. Results: In the case of inputting extremely low resolution (6 × 6) facial expression images, the accuracy rate is increased by 9.091%. The accuracy of the high-resolution facial expressions after reconstruction with a size of 12×12 is 96.970%. The accuracy rate for happy expressions is 100%, the accuracy rate for anger, disgust, sadness, and surprise recognition is 97%, the accuracy rate for contempt is 94%, and the accuracy rate for fear is 88%. Conclusions: The experimental results verify the feasibility and superiority of the system, and effectively improve the accuracy of low-resolution facial expressions.