Abstract
Facial expression recognition is one of the most challenging research area in the image recognition field and has been studied actively for a long time. Especially, we think that smile is important facial expression to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate its intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we achieved both low calculation cost and high performance with practical images and we also implemented the proposed methods to the PC demonstration system.
| Original language | English |
|---|---|
| Title of host publication | Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers |
| Pages | 277-286 |
| Number of pages | 10 |
| Edition | PART1 |
| DOIs | |
| Publication status | Published - Sep 28 2011 |
| Event | International Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand Duration: Nov 8 2010 → Nov 9 2010 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART1 |
| Volume | 6468 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | International Workshops on Computer Vision, ACCV 2010 |
|---|---|
| Country | New Zealand |
| City | Queenstown |
| Period | 11/8/10 → 11/9/10 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
Appearance-based smile intensity estimation by cascaded support vector machines. / Shimada, Keiji; Matsukawa, Tetsu; Noguchi, Yoshihiro; Kurita, Takio.
Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers. PART1. ed. 2011. p. 277-286 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6468 LNCS, No. PART1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Appearance-based smile intensity estimation by cascaded support vector machines
AU - Shimada, Keiji
AU - Matsukawa, Tetsu
AU - Noguchi, Yoshihiro
AU - Kurita, Takio
PY - 2011/9/28
Y1 - 2011/9/28
N2 - Facial expression recognition is one of the most challenging research area in the image recognition field and has been studied actively for a long time. Especially, we think that smile is important facial expression to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate its intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we achieved both low calculation cost and high performance with practical images and we also implemented the proposed methods to the PC demonstration system.
AB - Facial expression recognition is one of the most challenging research area in the image recognition field and has been studied actively for a long time. Especially, we think that smile is important facial expression to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate its intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we achieved both low calculation cost and high performance with practical images and we also implemented the proposed methods to the PC demonstration system.
UR - http://www.scopus.com/inward/record.url?scp=80053098027&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053098027&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-22822-3_28
DO - 10.1007/978-3-642-22822-3_28
M3 - Conference contribution
AN - SCOPUS:80053098027
SN - 9783642228216
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 277
EP - 286
BT - Computer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
ER -