An Online Extraction Algorithm for Image Feature Information Based on Convolutional Neural Network

• Published in: Mobile information systems Vol. 2022; pp. 1 - 11
• Main Author: Wei, Dahuan
• Format: Journal Article
• Language: English
• Published: Amsterdam Hindawi 20.05.2022; Hindawi Limited
• Subjects: Algorithms; Artificial neural networks; Classification; Datasets; Deep learning; Distance learning; Face recognition; Feature extraction; Feature recognition; Information retrieval; Neural networks; Object recognition; Performance evaluation; Principles; Teaching methods
• ISSN: 1574-017X; 1875-905X
• DOI: 10.1155/2022/1606438

As a result of fast technological improvement and the rise of online social media, image data have grown rapidly. Because of their rich content and intuitiveness as one of the key modes of people's daily communication, as a result, images are often used as communication vehicles. When it comes to image recognition, picture feature extraction is a critical stage, and the effect of image feature extraction directly impacts the effectiveness of image recognition. Furthermore, feature extraction is a key factor to consider that influences picture recognition accuracy. Unfortunately, due to the effects of individual variations and lighting, certain elements that are significantly connected to alterations in the image are hard to extract. As a result, features that appropriately show the changing interface are urgently needed. For this purpose, this research proposes an expression identification approach based on a deep convolutional neural network for the job of facial expression recognition under online picture feature information extraction. It uses the VGG19 and Resnet18 to recognize and classify facial expressions. After that, the DCNs have combined feature extraction and classification into a single network using deep convolutional neural networks (DCNs). The proposed model is compared to the most recent approach in the context of the FER2013 and CK + databases. The experimental results reveal that this method outperforms the competition, and the amount of useful image feature information that can be extracted is substantial.