人工智能
面部表情
线性判别分析
模式识别(心理学)
聚类分析
计算机科学
判别式
面子(社会学概念)
相似性(几何)
局部二进制模式
余弦相似度
特征(语言学)
表达式(计算机科学)
闭塞
离散余弦变换
特征提取
计算机视觉
图像(数学)
医学
社会科学
语言学
哲学
社会学
心脏病学
程序设计语言
直方图
作者
Jordan Vice,Masood Mehmood Khan,Iain Murray,Svetlana Yanushkevich
标识
DOI:10.1109/eais51927.2022.9787693
摘要
Internationally, the recent pandemic caused severe social changes forcing people to adopt new practices in their daily lives. One of these changes requires people to wear masks in public spaces to mitigate the spread of viral diseases. Affective state assessment (ASA) systems that rely on facial expression analysis become impaired and less effective due to the presence of visual occlusions caused by wearing masks. Therefore, ASA systems need to be future-proofed and equipped with adaptive technologies to be able to analyze and assess occluded facial expressions, particularly in the presence of masks. This paper presents an adaptive approach for classifying occluded facial expressions when human faces are partially covered with masks. We deployed an unsupervised, cosine similarity-based clustering approach exploiting the continuous nature of the extended Cohn-Kanade (CK+) dataset. The cosine similarity-based clustering resulted in twenty-one micro-expression clusters that describe minor variations of human facial expressions. Linear discriminant analysis was used to project all clusters onto lower-dimensional discriminant feature spaces, allowing for binary occlusion classification and the dynamic assessment of affective states. During the validation stage, we observed 100% accuracy when classifying faces with features extracted from the lower part of the occluded faces (occlusion detection). We observed 76.11% facial expression classification accuracy when features were gathered from the uncovered full-faces and 73.63% classification accuracy when classifying upper-facial expressions - applied when the lower part of the face is occluded. The presented system promises an improvement to visual inspection systems through an adaptive occlusion detection and facial expression classification framework.
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