Jucheng Yang

Tianjin University of Technology, China

Jucheng Yang is a Distinguished Professor at the College of Artificial Intelligence, Tianjin University of Science and Technology, China. He was a visiting professor at the University of New South Wales, Australia, and the University of Surrey, UK. He has published more than 140 papers in international journals and conferences. He has served as an associate editor of Frontiers in Plant Science and Journal of Information Security and Applications and a reviewer of international journals such as IEEE Transactions on Information Forensics and Security, IEEE Transactions on Industrial Informatics, and IEEE Communications Magazine. He has served as the chairman of eight conferences and a member of the program committees of more than thirty conferences. His research interests include biometrics, image processing, neural networks, and information security.

2books edited

2chapters authored

Latest work with IntechOpen by Jucheng Yang

Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained widespread attention and applications in fields such as natural language processing, medical image analysis, and Internet of Things (IoT) device recognition. This book, Deep Learning and Reinforcement Learning examines the latest research achievements of these technologies and provides a reference for researchers, engineers, students, and other interested readers. It helps readers understand the opportunities and challenges faced by deep learning and reinforcement learning and how to address them, thus improving the research and application capabilities of these technologies in related fields.

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Deep Learning and Reinforcement Learning

Edited by Jucheng Yang

Books edited

Machine Learning and Biometrics

Edited by Jucheng Yang

Chapters authored

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

By Jucheng Yang, Lingchao Zhang, Meng Li, Tingting Zhao, Yarui Chen, Jianzheng Liu and Na Liu

Facial occlusion is a difficulty in the field of face recognition. The lack of features caused by occlusion may reduce the face recognition rate greatly. How to extract the identified features from the occluded faces has a profound effect on face recognition. This chapter presents a Local Cycle Graph Structure (LCGS) operator, which makes full use of the information of the pixels around the target pixel with its neighborhood of 3 × 3. Thus, the recognition with the extracted features is more efficient. We apply the extreme learning machine (ELM) classifier to train and test the features extracted by LCGS algorithm. In the experiment, we use the olivetti research laboratory (ORL) database to simulate occlusion randomly and use the AR database for physical occlusion. Physical coverings include scarves and sunglasses. Experimental results demonstrate that our algorithm yields a state-of-the-art performance.

Part of the book: Machine Learning and Biometrics

Introductory Chapter: Machine Learning and Biometrics

By Jucheng Yang, Yarui Chen, Chuanlei Zhang, Dong Sun Park and Sook Yoon

Part of the book: Machine Learning and Biometrics

Jucheng Yang

Latest work with IntechOpen by Jucheng Yang

Deep Learning and Reinforcement Learning

Books edited

Machine Learning and Biometrics

Chapters authored

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