Face classification with support vector machine

Date

2004-04-30

Author

Kepenekci, B
Akar, Gözde

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A new approach to feature based frontal face recognition with Gabor wavelets and support vector machines is presented in this paper. The feature points are automatically extracted using the local characteristics of each individual face. A kernel that computes the similarity between two feature vectors, is used to map the face features to a space with higher dimension. To find the identity of a test face, the possible labels of each feature vector of that face is found with support vector machines, then the last decision is made by considering all of those labels. By using Gabor features the number of support vectors is reduced compared to directly using the actual image data, and. also a better generalization performance is achieved.

Subject Keywords

recognition

URI

https://hdl.handle.net/11511/36709

DOI

https://doi.org/10.1109/siu.2004.1338596

Conference Name

IEEE 12th Signal Processing and Communications Applications Conference

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

B. Kepenekci and G. Akar, “Face classification with support vector machine,” presented at the IEEE 12th Signal Processing and Communications Applications Conference, Kusadasi, TURKEY, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36709.