Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns
In recent years, research in automated facial expression recognition has attained significant attention for its potential applicability in human-computer interaction, surveillance systems, animation, and consumer electronics. However, recognition in uncontrolled environments under the presence of il...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2018-06-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amcs-2018-0030 |
| _version_ | 1818586359872356352 |
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| author | Ahmed Faisal Kabir Md. Hasanul |
| author_facet | Ahmed Faisal Kabir Md. Hasanul |
| author_sort | Ahmed Faisal |
| collection | DOAJ |
| description | In recent years, research in automated facial expression recognition has attained significant attention for its potential applicability in human-computer interaction, surveillance systems, animation, and consumer electronics. However, recognition in uncontrolled environments under the presence of illumination and pose variations, low-resolution video, occlusion, and random noise is still a challenging research problem. In this paper, we investigate recognition of facial expression in difficult conditions by means of an effective facial feature descriptor, namely the directional ternary pattern (DTP). Given a face image, the DTP operator describes the facial feature by quantizing the eight-directional edge response values, capturing essential texture properties, such as presence of edges, corners, points, lines, etc. We also present an enhancement of the basic DTP encoding method, namely the compressed DTP (cDTP) that can describe the local texture more effectively with fewer features. The recognition performances of the proposed DTP and cDTP descriptors are evaluated using the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, we simulate difficult conditions using original database images with lighting variations, low-resolution images obtained by down-sampling the original, and images corrupted with Gaussian noise. In all cases, the proposed method outperforms some of the well-known face feature descriptors. |
| first_indexed | 2024-12-16T08:51:43Z |
| format | Article |
| id | doaj.art-d1579f08e7b3432583e435d74c955569 |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-12-16T08:51:43Z |
| publishDate | 2018-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-d1579f08e7b3432583e435d74c9555692022-12-21T22:37:24ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922018-06-0128239940910.2478/amcs-2018-0030amcs-2018-0030Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture PatternsAhmed Faisal0Kabir Md. Hasanul1Department of Computer Science University of Calgary, 2500 University Drive NW,Calgary, AB, CanadaDepartment of Computer Science and Engineering Islamic University of Technology, Board Bazar,Gazipur1704, BangladeshIn recent years, research in automated facial expression recognition has attained significant attention for its potential applicability in human-computer interaction, surveillance systems, animation, and consumer electronics. However, recognition in uncontrolled environments under the presence of illumination and pose variations, low-resolution video, occlusion, and random noise is still a challenging research problem. In this paper, we investigate recognition of facial expression in difficult conditions by means of an effective facial feature descriptor, namely the directional ternary pattern (DTP). Given a face image, the DTP operator describes the facial feature by quantizing the eight-directional edge response values, capturing essential texture properties, such as presence of edges, corners, points, lines, etc. We also present an enhancement of the basic DTP encoding method, namely the compressed DTP (cDTP) that can describe the local texture more effectively with fewer features. The recognition performances of the proposed DTP and cDTP descriptors are evaluated using the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database. In our experiments, we simulate difficult conditions using original database images with lighting variations, low-resolution images obtained by down-sampling the original, and images corrupted with Gaussian noise. In all cases, the proposed method outperforms some of the well-known face feature descriptors.https://doi.org/10.2478/amcs-2018-0030directional ternary patterncompressed dtpfacial feature descriptortexture encodingsupport vector machine |
| spellingShingle | Ahmed Faisal Kabir Md. Hasanul Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns International Journal of Applied Mathematics and Computer Science directional ternary pattern compressed dtp facial feature descriptor texture encoding support vector machine |
| title | Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns |
| title_full | Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns |
| title_fullStr | Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns |
| title_full_unstemmed | Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns |
| title_short | Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns |
| title_sort | facial expression recognition under difficult conditions a comprehensive study on edge directional texture patterns |
| topic | directional ternary pattern compressed dtp facial feature descriptor texture encoding support vector machine |
| url | https://doi.org/10.2478/amcs-2018-0030 |
| work_keys_str_mv | AT ahmedfaisal facialexpressionrecognitionunderdifficultconditionsacomprehensivestudyonedgedirectionaltexturepatterns AT kabirmdhasanul facialexpressionrecognitionunderdifficultconditionsacomprehensivestudyonedgedirectionaltexturepatterns |