ADReFV: Face video dataset based on human‐computer interaction for Alzheimer's disease recognition. (18th December 2022)

• Record Type: Journal Article
• Authors: Xu, Tao; Wang, Xinheng; Lun, Xie; Pan, Hang; Wang, Zhiliang
• Other Names: Kim Jinman guestEditor.; Papagiannakis George guestEditor.; Sheng Bin guestEditor.; Thalmann Daniel guestEditor.
• Is Part Of: Computer animation and virtual worlds. Volume 34:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-18
• Subjects: Alzheimer's disease -- assisted assessment -- attention mechanisms -- facial video dataset -- human‐computer interaction; Computer animation -- Periodicals; Visualization -- Periodicals; 006.6
• DOI: 10.1002/cav.2127
• Languages: English
• ISSNs: 1546-4261

Abstract

Abstract: With the global aging problem becoming more and more serious, the initial screening for Alzheimer's disease (AD) will become increasingly important. We understand that facial expressions are related to the severity of dementia, but there is no face‐related data in the existing Alzheimer's dataset. This article attempts to establish a facial video‐based AD recognition dataset through a human‐computer interaction method. This interactive task was designed for AD in attention, execution, visual space ability, facial apraxia, and facial changes in task success and failure. Using this task as the collection method, the final dataset includes 102 faces video data, specific task scores, and emotional self‐evaluation. For baseline evaluation, the improved local binary pattern on three orthogonal planes and RF were employed respectively for feature extraction and classification with the 5‐fold cross‐validation method. The best performance was 76.00% for 3‐class classification. In addition, a frame attention network based on fine‐grained local region localization was proposed, which improved the accuracy of cognitive classification to 84.45%. Finally, the analysis was conducted for the association of expressions with cognition and emotion in the AD dataset. This study aims to solve the current lack of standards for AD in the field of facial recognition and contribute to future research and clinical applications. Abstract : In "ADReFV: face video dataset based on human‐computer interaction for Alzheimer's disease recognition, " for the large‐scale prescreening problem of Alzheimer's disease (AD), Xu et al. use a human‐computer interactive system to capture facial feature data and assist in assessment through deep learning. A frame attention network based on fine‐grained local area localization is used to further improve accuracy and to analyze the association of expression with cognition and emotion in AD patients.