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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Li Fei Wang Yajie Qiu Hongkun Wang Linlin |
| Copyright Year | 2014 |
| Description | Author affiliation: Eng. Training Center, Shenyang Aerosp. Univ., Shenyang, China (Li Fei; Wang Yajie; Qiu Hongkun; Wang Linlin) |
| Abstract | Gender identification is a new domain in image recognition. Gender identification of human face is to judge one's gender according to his/her face features. The article adopted local binary pattern (LBP) algorithm to build feature subspaces, and processed data using Support Vector Machine (SVM) learning models. Experiments showed that integration of LBP algorithm with linear SVM and integration of LBP algorithm with low-order polynomial SVM are better than Gauss-kernel function. |
| Starting Page | 444 |
| Ending Page | 448 |
| File Size | 278725 |
| Page Count | 5 |
| File Format | |
| e-ISBN | 9781479968541 |
| DOI | 10.1109/ICVRV.2014.77 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-30 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Local binary pattern Human face images Kernel functions Gender identification Feature extraction Polynomials Classification algorithms Support vector machine Face Kernel |
| Content Type | Text |
| Resource Type | Article |
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