Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition
In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for recognition of emotions from facial features. Local binary patterns have been proven to effectively describe the statistical characteristics of face image as it contains information related to edges, spots, etc. The...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98286 http://hdl.handle.net/10220/12391 |
| _version_ | 1811683573271363584 |
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| author | Suresh, Sundaram Subramanian, K. Venkatesh Babu, R. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Suresh, Sundaram Subramanian, K. Venkatesh Babu, R. |
| author_sort | Suresh, Sundaram |
| collection | NTU |
| description | In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for recognition of emotions from facial features. Local binary patterns have been proven to effectively describe the statistical characteristics of face image as it contains information related to edges, spots, etc. The aim of McFIS is to approximate the functional relationship between the facial features and various emotions. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: a) sample deletion b) sample learning and c) sample reserve. Performance of proposed McFIS based facial emotion recognition is evaluated on LBP features extracted from JAFFE database. The simulation results are compared with support vector machine classifier and other results available in literature. The results indicate the superior performance of McFIS in comparison to other algorithms. |
| first_indexed | 2024-10-01T04:14:53Z |
| format | Conference Paper |
| id | ntu-10356/98286 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:14:53Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/982862020-05-28T07:18:10Z Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition Suresh, Sundaram Subramanian, K. Venkatesh Babu, R. School of Computer Engineering International Joint Conference on Neural Networks (2012 : Brisbane, Australia) DRNTU::Engineering::Computer science and engineering In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for recognition of emotions from facial features. Local binary patterns have been proven to effectively describe the statistical characteristics of face image as it contains information related to edges, spots, etc. The aim of McFIS is to approximate the functional relationship between the facial features and various emotions. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: a) sample deletion b) sample learning and c) sample reserve. Performance of proposed McFIS based facial emotion recognition is evaluated on LBP features extracted from JAFFE database. The simulation results are compared with support vector machine classifier and other results available in literature. The results indicate the superior performance of McFIS in comparison to other algorithms. 2013-07-26T06:34:24Z 2019-12-06T19:53:11Z 2013-07-26T06:34:24Z 2019-12-06T19:53:11Z 2012 2012 Conference Paper Subramanian, K., Suresh, S., & Venkatesh Babu, R. (2012). Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition. The 2012 International Joint Conference on Neural Networks (IJCNN). https://hdl.handle.net/10356/98286 http://hdl.handle.net/10220/12391 10.1109/IJCNN.2012.6252678 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Suresh, Sundaram Subramanian, K. Venkatesh Babu, R. Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title | Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title_full | Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title_fullStr | Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title_full_unstemmed | Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title_short | Meta-Cognitive Neuro-Fuzzy Inference System for human emotion recognition |
| title_sort | meta cognitive neuro fuzzy inference system for human emotion recognition |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/98286 http://hdl.handle.net/10220/12391 |
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