Fusing multiple features for Fourier Mellin-based face recognition with single example image per person

Chen, Yee Ming; Chiang, Jen-Hong

doi:10.1016/j.neucom.2010.06.007

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…We have compared the performance of the proposed combined descriptors with other popular methods such as PCA, 2DPCA, (PC) 2 A, E(PC) 2 A, 2D(PC) 2 A, SVD perturbation [38], and hybrid Fourier-AFMT transform [39] for face recognition with single (first) example image per person. As shown in Table 11, the proposed combined descriptors give the best recognition rate when compared with other well-established methods.…”

Section: Performance Comparisonmentioning

confidence: 99%

Complementary feature sets for optimal face recognition

2014

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In face recognition tasks, one kind of feature set is not adequate to generate superior results; thus, selection and combination of complementary features are crucial steps. In this paper, the fusion of two useful descriptors, i.e., the Zernike moments (ZMs) and the local binary pattern (LBP)/local ternary pattern (LTP), has been proposed. The ZM descriptor consists of good global image representation capabilities besides being invariant to image rotation and noise, while the LBP/LTP descriptors capture the innate details within some local parts of face image and are insensitive to illumination variations. The fusion of these two is observed to incorporate the traits of both of these individual descriptors. Subsequently, in this work, the performance of diverse feature sets of ZMs (i.e., magnitude features, magnitude plus phase features, and the real plus imaginary component features) combined with the LBP/LTP descriptor is analyzed on FERET, Yale, and ORL face databases. The recognition results achieved by the proposed method are approximately 10 to 30% higher than those obtained with these descriptors separately. Recognition rates of the proposed method are also found to be significantly better (i.e., by 8 to 24%) in case of single example image per person in the training.

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Section: Performance Comparisonmentioning

confidence: 99%

Complementary feature sets for optimal face recognition

2014

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“…With the increased attention from researchers, many methods have been proposed in the literature [3][4][5][6][7][8][9][10]. However, there are still many challenges need to be faced in FR field.…”

Section: Introductionmentioning

confidence: 99%

Variational Feature Representation-based Classification for face recognition with single sample per person

Ding

Huang

et al. 2015

Journal of Visual Communication and Image Representation

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“…[2]. Usually the features extracted from the images are presented as multi-dimensional points, and the distances between corresponding multi-dimensional points are calculated by some distance metrics to measure the similarity between the two points in the multi-dimensional feature space [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Content Based Image Retrieval Model Using Feature Space Dividing

Huang

2014

AMM

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This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

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Fusing multiple features for Fourier Mellin-based face recognition with single example image per person

Cited by 13 publications

References 22 publications

Complementary feature sets for optimal face recognition

Complementary feature sets for optimal face recognition

Variational Feature Representation-based Classification for face recognition with single sample per person

A Content Based Image Retrieval Model Using Feature Space Dividing

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