A Novel Facial Feature Extraction Method Based on ICM Network for Affective Recognition
Year
: 2011
Abstract: This paper presents a facial expression recognition
approach to recognize the affective states. Feature extraction is
a vital step in the recognition of facial expressions. In this work,
a novel facial feature extraction method based on Intersecting
Cortical Model (ICM) is proposed. The ICM network which is
a simplified model of Pulse-Coupled Neural Network (PCNN)
model has great potential to perform pixel grouping. In the
proposed method the normalized face image is segmented into
two regions including mouth, eyes using fuzzy c-means
clustering (FCM). Segmented face images are imported into an
ICM network with 300 iteration number and pulse image
produced by the ICM network is chosen as the face code, then
the support vector machine (SVM) is trained for discrimination
of different expressions to distinguish the different affective
states. In order to evaluate the performance of the proposed
algorithm, the face image dataset is constructed and the
proposed algorithm is used to classify seven basic expressions
including happiness, sadness, fear, anger, surprise and hate
The experimental results confirm that ICM network has great
potential for facial feature extraction and the proposed method
for human affective recognition is promising. Fast feature
extraction is the most advantage of this method which can be
useful for real world application.
approach to recognize the affective states. Feature extraction is
a vital step in the recognition of facial expressions. In this work,
a novel facial feature extraction method based on Intersecting
Cortical Model (ICM) is proposed. The ICM network which is
a simplified model of Pulse-Coupled Neural Network (PCNN)
model has great potential to perform pixel grouping. In the
proposed method the normalized face image is segmented into
two regions including mouth, eyes using fuzzy c-means
clustering (FCM). Segmented face images are imported into an
ICM network with 300 iteration number and pulse image
produced by the ICM network is chosen as the face code, then
the support vector machine (SVM) is trained for discrimination
of different expressions to distinguish the different affective
states. In order to evaluate the performance of the proposed
algorithm, the face image dataset is constructed and the
proposed algorithm is used to classify seven basic expressions
including happiness, sadness, fear, anger, surprise and hate
The experimental results confirm that ICM network has great
potential for facial feature extraction and the proposed method
for human affective recognition is promising. Fast feature
extraction is the most advantage of this method which can be
useful for real world application.
Keyword(s): A Novel Facial Feature Extraction Method Based
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A Novel Facial Feature Extraction Method Based on ICM Network for Affective Recognition
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| contributor author | F. Mokhayeri | en |
| contributor author | محمدرضا اکبرزاده توتونچی | en |
| contributor author | Mohammad Reza Akbarzadeh Totonchi | fa |
| date accessioned | 2020-06-06T14:05:00Z | |
| date available | 2020-06-06T14:05:00Z | |
| date copyright | 7/31/2011 | |
| date issued | 2011 | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3381673 | |
| description abstract | This paper presents a facial expression recognition approach to recognize the affective states. Feature extraction is a vital step in the recognition of facial expressions. In this work, a novel facial feature extraction method based on Intersecting Cortical Model (ICM) is proposed. The ICM network which is a simplified model of Pulse-Coupled Neural Network (PCNN) model has great potential to perform pixel grouping. In the proposed method the normalized face image is segmented into two regions including mouth, eyes using fuzzy c-means clustering (FCM). Segmented face images are imported into an ICM network with 300 iteration number and pulse image produced by the ICM network is chosen as the face code, then the support vector machine (SVM) is trained for discrimination of different expressions to distinguish the different affective states. In order to evaluate the performance of the proposed algorithm, the face image dataset is constructed and the proposed algorithm is used to classify seven basic expressions including happiness, sadness, fear, anger, surprise and hate The experimental results confirm that ICM network has great potential for facial feature extraction and the proposed method for human affective recognition is promising. Fast feature extraction is the most advantage of this method which can be useful for real world application. | en |
| language | English | |
| title | A Novel Facial Feature Extraction Method Based on ICM Network for Affective Recognition | en |
| type | Conference Paper | |
| contenttype | External Fulltext | |
| subject keywords | A Novel Facial Feature Extraction Method Based | en |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1026601.html | |
| conference title | Proceedings of International Joint Conference on Neural Networks, San Jos | en |
| identifier articleid | 1026601 |