A review of automated micro-expression analysis
Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20578/1/02.pdf |
| _version_ | 1796933277057024000 |
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| author | Koo, Sie Min Mohd Asyraf Zulkifley, Nor Azwan Mohamed Kamari, |
| author_facet | Koo, Sie Min Mohd Asyraf Zulkifley, Nor Azwan Mohamed Kamari, |
| author_sort | Koo, Sie Min |
| collection | UKM |
| description | Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the
expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and
therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression
recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focused on the deep
learning approach. Compared to traditional machine learning, which relies on manual feature processing and requires
the use of formulated rules, deep learning networks produce more accurate micro-expression recognition performances
through an end-to-end methodology, whereby the features of interest were extracted optimally through the training process,
utilizing a large set of data. This paper reviews the developments and trends in micro-expression recognition from the
earlier studies (hand-crafted approach) to the present studies (deep learning approach). Some of the important topics
that will be covered include the detection of micro-expression from short videos, apex frame spotting, micro-expression
recognition as well as performance discussion on the reviewed methods. Furthermore, major limitations that hamper
the development of automated micro-expression recognition systems are also analyzed, followed by recommendations of
possible future research directions. |
| first_indexed | 2024-03-06T04:44:45Z |
| format | Article |
| id | ukm.eprints-20578 |
| institution | Universiti Kebangsaan Malaysia |
| language | English |
| last_indexed | 2024-03-06T04:44:45Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | dspace |
| spelling | ukm.eprints-205782022-11-28T12:10:40Z http://journalarticle.ukm.my/20578/ A review of automated micro-expression analysis Koo, Sie Min Mohd Asyraf Zulkifley, Nor Azwan Mohamed Kamari, Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focused on the deep learning approach. Compared to traditional machine learning, which relies on manual feature processing and requires the use of formulated rules, deep learning networks produce more accurate micro-expression recognition performances through an end-to-end methodology, whereby the features of interest were extracted optimally through the training process, utilizing a large set of data. This paper reviews the developments and trends in micro-expression recognition from the earlier studies (hand-crafted approach) to the present studies (deep learning approach). Some of the important topics that will be covered include the detection of micro-expression from short videos, apex frame spotting, micro-expression recognition as well as performance discussion on the reviewed methods. Furthermore, major limitations that hamper the development of automated micro-expression recognition systems are also analyzed, followed by recommendations of possible future research directions. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20578/1/02.pdf Koo, Sie Min and Mohd Asyraf Zulkifley, and Nor Azwan Mohamed Kamari, (2022) A review of automated micro-expression analysis. Jurnal Kejuruteraan, 34 (5). pp. 763-775. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3405-2022/ |
| spellingShingle | Koo, Sie Min Mohd Asyraf Zulkifley, Nor Azwan Mohamed Kamari, A review of automated micro-expression analysis |
| title | A review of automated micro-expression analysis |
| title_full | A review of automated micro-expression analysis |
| title_fullStr | A review of automated micro-expression analysis |
| title_full_unstemmed | A review of automated micro-expression analysis |
| title_short | A review of automated micro-expression analysis |
| title_sort | review of automated micro expression analysis |
| url | http://journalarticle.ukm.my/20578/1/02.pdf |
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