51. Face Recognition Using Composite Features Based on Discriminant Analysis
- Author
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Won-Yong Shin, Sung-Sin Lee, Sang-Il Choi, and Sang Tae Choi
- Subjects
General Computer Science ,Computer science ,Composite feature ,Feature vector ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,local-feature ,Facial recognition system ,Image (mathematics) ,feature selection ,Discriminative model ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,business.industry ,General Engineering ,020207 software engineering ,Pattern recognition ,discriminant analysis ,Linear discriminant analysis ,Face (geometry) ,holistic-feature ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 ,face recognition - Abstract
Extracting holistic features from the whole face and extracting the local features from the sub-image have pros and cons depending on the conditions. In order to effectively utilize the strengths of various types of holistic features and local features while also complementing each weakness, we propose a method to construct a composite feature vector for face recognition based on discriminant analysis. We first extract the holistic features and the local features from the whole face image and various types of local images using the discriminant feature extraction method. Then, we measure the amount of discriminative information in the individual holistic features and local features and construct composite features with only discriminative features for face recognition. The composite features from the proposed method were compared with the holistic features, local features, and others prepared by hybrid methods through face recognition experiments for various types of face image databases. The proposed composite feature vector displayed better performance than the other methods.
- Published
- 2018
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