Adaptive Classification of Occluded Facial Expressions of Affective States
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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
IEEE Press
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/88712 |
| _version_ | 1848765070378532864 |
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| author | Vice, Jordan Khan, Masood Murray, Iain Yanushkevich, Svetlana |
| author2 | Papadopoulos, George |
| author_facet | Papadopoulos, George Vice, Jordan Khan, Masood Murray, Iain Yanushkevich, Svetlana |
| author_sort | Vice, Jordan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 similaritybased
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. |
| first_indexed | 2025-11-14T11:29:24Z |
| format | Conference Paper |
| id | curtin-20.500.11937-88712 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:29:24Z |
| publishDate | 2022 |
| publisher | IEEE Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-887122022-06-21T02:38:39Z Adaptive Classification of Occluded Facial Expressions of Affective States Vice, Jordan Khan, Masood Murray, Iain Yanushkevich, Svetlana Papadopoulos, George Angelov, Plamen 0915 - Interdisciplinary Engineering 4611 - Machine learning 4602 - Artificial intelligence 4603 - Computer vision and multimedia computation 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 similaritybased 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. 2022 Conference Paper http://hdl.handle.net/20.500.11937/88712 10.1109/EAIS51927.2022.9787693 English IEEE Press restricted |
| spellingShingle | 0915 - Interdisciplinary Engineering 4611 - Machine learning 4602 - Artificial intelligence 4603 - Computer vision and multimedia computation Vice, Jordan Khan, Masood Murray, Iain Yanushkevich, Svetlana Adaptive Classification of Occluded Facial Expressions of Affective States |
| title | Adaptive Classification of Occluded Facial Expressions of Affective States |
| title_full | Adaptive Classification of Occluded Facial Expressions of Affective States |
| title_fullStr | Adaptive Classification of Occluded Facial Expressions of Affective States |
| title_full_unstemmed | Adaptive Classification of Occluded Facial Expressions of Affective States |
| title_short | Adaptive Classification of Occluded Facial Expressions of Affective States |
| title_sort | adaptive classification of occluded facial expressions of affective states |
| topic | 0915 - Interdisciplinary Engineering 4611 - Machine learning 4602 - Artificial intelligence 4603 - Computer vision and multimedia computation |
| url | http://hdl.handle.net/20.500.11937/88712 |