Face recognition using recursive fisher linear discriminant with gabor wavelet coding

Chen, Xiang; Fan, Xiaotian; Lee, T.H.

doi:10.1109/icip.2004.1418694

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The facial expression recognition performance was improved dramatically by RCLD. Compared to the lowest error rate of 32.69% for six-expression recognition reported in [7], it is evident that RCLD provides a much more powerful tool for feature extraction.…”

Section: Facial Expression Recognitionmentioning

confidence: 85%

“…W S is made non-singular by first concatenating all previously extracted feature vectors to W S and then performing some matrix transformation. It was shown in [7] that RFLD achieved significant improvement for face recognition problem compared to other methods. Although perfect recognition result was obtained for identity recognition problem, the recognition error rate for facial expression recognition problem is around 30%, which is too high compared to the capacities of human beings.…”

Section: Recursive Fisher Linear Discriminant (Rfld)mentioning

confidence: 93%

“…) )( ( 1 (7) which implies that maximization of criterion (1) would make the means (i.e., the prototypes) for different classes as far away as possible while keep the members within each class as close as possible.…”

Section: Fisher Linear Discriminant (Fld)mentioning

confidence: 99%

“…RFLD was recently proposed by Xiang, Fan and Lee [7] to overcome the feature number constraint using a recursive procedure. It extracts only one feature vector at each iteration.…”

Section: Recursive Fisher Linear Discriminant (Rfld)mentioning

confidence: 99%

“…To overcome this limitation, a recursive procedure was recently proposed by Xiang, Fan and Lee [7], which was demonstrated to improve the recognition rate substantially. However, it is a computational intensive algorithm since only one feature is extracted at each iteration.…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition Using Recursive Cluster-Based Linear Discriminant

Chen

Huang

2005

2005 IEEE 7th Workshop on Multimedia Signal Processing

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Two new recursive procedures for extracting discriminant features, termed Recursive Modified Linear Discriminant (RMLD) and Recursive Cluster-based Linear Discriminant (RCLD) are proposed in this paper. The two new methods, RMLD and RCLD overcome two major shortcomings of Fisher Linear Discriminant (FLD): it can fully exploit all information available for discrimination; and it removes the constraint on the total number of features that can be extracted. Experiments of comparing the new algorithm with the traditional FLD and some of its variations have been carried out on various types of face recognition problems for Yale database, in which the resulting improvement of the performances by the new feature extraction scheme is significant.

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Section: Facial Expression Recognitionmentioning

confidence: 85%

Section: Recursive Fisher Linear Discriminant (Rfld)mentioning

confidence: 93%

Section: Fisher Linear Discriminant (Fld)mentioning

confidence: 99%

“…RFLD was recently proposed by Xiang, Fan and Lee [7] to overcome the feature number constraint using a recursive procedure. It extracts only one feature vector at each iteration.…”

Section: Recursive Fisher Linear Discriminant (Rfld)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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