Abstract
To reduce the rate of error in gender classification, we propose the use of an integration framework that uses conventional facial images along with neck images. First, images are separated into facial and neck regions, and features are extracted from monochrome, color, and edge images of both regions. Second, we use Support Vector Machines (SVMs) to classify the gender of each individual feature. Finally, we reclassify the gender by considering the six types of distances from the optimal separating hyperplane as a 6-dimensional vector. Experimental results show a 28.4% relative reduction in error over the performance baseline of the monochrome facial image approach, which until now had been considered to have the most accurate performance.
| Original language | English |
|---|---|
| Pages (from-to) | 1803-1809 |
| Number of pages | 7 |
| Journal | Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers |
| Volume | 61 |
| Issue number | 12 |
| Publication status | Published - 2007 Dec |
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Keywords
- Feature extraction
- Gender classification
- Image processing
- Pattern recognition
- SVM
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Computer Vision and Pattern Recognition
Cite this
Gender classification based on integration of multiple classifiers using various features of facial and neck images. / Ueki, Kazuya; Kobayashi, Tetsunori.
In: Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, Vol. 61, No. 12, 12.2007, p. 1803-1809.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Gender classification based on integration of multiple classifiers using various features of facial and neck images
AU - Ueki, Kazuya
AU - Kobayashi, Tetsunori
PY - 2007/12
Y1 - 2007/12
N2 - To reduce the rate of error in gender classification, we propose the use of an integration framework that uses conventional facial images along with neck images. First, images are separated into facial and neck regions, and features are extracted from monochrome, color, and edge images of both regions. Second, we use Support Vector Machines (SVMs) to classify the gender of each individual feature. Finally, we reclassify the gender by considering the six types of distances from the optimal separating hyperplane as a 6-dimensional vector. Experimental results show a 28.4% relative reduction in error over the performance baseline of the monochrome facial image approach, which until now had been considered to have the most accurate performance.
AB - To reduce the rate of error in gender classification, we propose the use of an integration framework that uses conventional facial images along with neck images. First, images are separated into facial and neck regions, and features are extracted from monochrome, color, and edge images of both regions. Second, we use Support Vector Machines (SVMs) to classify the gender of each individual feature. Finally, we reclassify the gender by considering the six types of distances from the optimal separating hyperplane as a 6-dimensional vector. Experimental results show a 28.4% relative reduction in error over the performance baseline of the monochrome facial image approach, which until now had been considered to have the most accurate performance.
KW - Feature extraction
KW - Gender classification
KW - Image processing
KW - Pattern recognition
KW - SVM
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UR - http://www.scopus.com/inward/citedby.url?scp=39049169153&partnerID=8YFLogxK
M3 - Article
VL - 61
SP - 1803
EP - 1809
JO - Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
JF - Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
SN - 1342-6907
IS - 12
ER -