DocumentCode
3014034
Title
A Face Recognition System Using Support Vector Machines and Elastic Graph Matching
Author
Li, Yun-Feng
Author_Institution
Electromech. Eng. Collage, Henan Univ. of Sci. & Technol., Luoyang, China
Volume
1
fYear
2009
fDate
7-8 Nov. 2009
Firstpage
3
Lastpage
6
Abstract
An efficient face recognition system by the combination of support vector machines (SVMs) and elastic graph matching was presented. The implementations of this system are as follows: Firstly, the two eyes of a face image are detected by using SVMs approach, and the detected eye coordinates are used as reference points for alignment and normalization of the face image. Secondly, elastic graph matching is performed to locate the facial feature points. The preprocessed face images have the same size and the eyes lie at the uniform positions, elastic graph matching is limited to local distortions only, so the matching time can be reduced considerably. Lastly, local facial features are extracted for face register or recognition, recognition is performed by comparing the similarity of the local facial features of a test image to all trained images. The experimental results demonstrate the effectiveness of this face recognition system.
Keywords
face recognition; feature extraction; graph theory; image matching; image registration; object detection; support vector machines; elastic graph matching; eye coordinate detection; face image normalization; face recognition system; face register; local facial feature point extraction; support vector machines; Artificial intelligence; Computational intelligence; Eyes; Face detection; Face recognition; Facial features; Image recognition; Performance evaluation; Support vector machines; Testing; Support Vector Machines; elastic graph matching; eye detection;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4244-3835-8
Electronic_ISBN
978-0-7695-3816-7
Type
conf
DOI
10.1109/AICI.2009.149
Filename
5375972
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