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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Moghaddam, B. Ming-Hsuan Yang |
| Copyright Year | 2000 |
| Description | Author affiliation: Mitsubishi Electr. Res. Lab., Cambridge, MA, USA (Moghaddam, B.) |
| Abstract | Support vector machines (SVM) are investigated for visual gender classification with low-resolution "thumbnail" faces (21-by-12 pixels) processed from 1755 images from the FERET face database. The performance of SVM (3.4% error) is shown to be superior to traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, nearest-neighbor) as well as more modern techniques such as radial basis function (RBF) classifiers and large ensemble-RBF networks. SVM also out-performed human test subjects at the same task: in a perception study with 30 human test subjects, ranging in age from mid-20s to mid-40s, the average error rate was found to be 32% for the "thumbnails" and 6.7% with higher resolution images. The difference in performance between low- and high-resolution tests with SVM was only 1%, demonstrating robustness and relative scale invariance for visual classification. |
| Starting Page | 306 |
| Ending Page | 311 |
| File Size | 143014 |
| Page Count | 6 |
| File Format | |
| ISBN | 0769505805 |
| DOI | 10.1109/AFGR.2000.840651 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-03-30 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Support vector machine classification Testing Humans Pixel Image databases Visual databases Error analysis Image resolution Robustness |
| Content Type | Text |
| Resource Type | Article |
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