NDLI: Features Fusion Based on FLD for Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Changjun Zhou Qiang Zhang Xiaopeng Wei Ziqi Wei
Copyright Year	2008
Description	Author affiliation: Liaoning Key Lab. of Intell. Inf. Process., Dalian Univ., Dalian, China (Changjun Zhou; Qiang Zhang; Xiaopeng Wei) \|\| Sch. of Inf., RenMin Univ. of China, Beijing, China (Ziqi Wei)
Abstract	In this paper, we introduced a features fusion method for face recognition based on Fisher's Linear Discriminant (FLD). The method extract features by employed Two-Dimensional principal component analysis (2DPCA) and Gabor wavelets, and then fuse their features which are extracted with FLD respectively. As a holistic feature extraction method, 2DPCA performs dimensional reduction to the input dataset while retaining characteristics of the dataset that contribute most to its variance by eliminating the later principal components. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with 2DPCA features. In addition to, because FLD could make not only the scatter between classes as large as possible, but the scatter within class as small as possible, the features which are extracted by FLD are reliable for classification. And then, the FLD features of the Gabor and 2DPCA features leads to the application of the support vector machine (SVM)for classification. Finally, the computer simulation illustrates the effectivity of this method on ORL face database.
Starting Page	654
Ending Page	658
File Size	449033
Page Count	5
File Format	PDF
ISBN	9780769535630
DOI	10.1109/ETTandGRS.2008.368
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Fuses Face recognition Scattering Support vector machine classification Feature extraction Wavelet analysis Linear discriminant analysis Application software Principal component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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