Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern

Mu, Meiru; Ruan, Qiuqi; Guo, Song

doi:10.1016/j.neucom.2011.05.026

Cited by 41 publications

(32 citation statements)

References 31 publications

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“…By constructing LBP type features from the amplitude and adopting different learning techniques, many Gabor filtering based approaches have shown remarkable advantages over pixel feature based methods: the identification rate in benchmark evaluations has been improved by more than 20% (reaching around 90%) thanks to the so-called "blessing of dimensionality" [Givens et al, 2013] ( but with a high cost of less computational efficiency [Mu et al, 2011,Chai et al, 2014). …”

Section: Related Workmentioning

confidence: 99%

How Good Can a Face Identifier Be Without Learning?

Yang

Hedman

2017

Communications in Computer and Information Science

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Section: Related Workmentioning

confidence: 99%

How Good Can a Face Identifier Be Without Learning?

Yang

Hedman

2017

Communications in Computer and Information Science

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“…The computational complexity is always a big concern. From Table 4 in (Mu et al, 2011), under the image size of 128 × 128 with a 5 × 8 Gabor filter bank, the histogram extraction of LGBP takes around 0.45 seconds, S[LGBP Mag+LGXP] takes 0.99 seconds. Extracting GOM feature takes 0.7 seconds (Chai et al, 2014).…”

Section: Evaluations On the Feret Databasementioning

confidence: 99%

“…This is because the amplitude varies slowly with spatial shift, making it robust to texture variations caused by dynamic expressions and imprecise alignment. By constructing LBP-type features mostly from the amplitude and applying various learning techniques, many Gabor based approaches have shown remarkable advantages over pixel-featured based methods: the identification rate in benchmark evaluations has been found to be improved by more than 20% (reaching around 90%) thanks to the "blessing of dimensionality" (Givens et al, 2013) ( but at the high cost of computational efficiency (Mu et al, 2011;Chai et al, 2014)). Now the question is how to achieve face identification rates in the range from 90% to 95% or even higher.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Yang

Liu

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:Gabor features have been widely employed in solving face recognition problems in controlled scenarios. To construct discriminative face features from the complex Gabor space, the amplitude information is commonly preferred, while the other one -the phase -is not well utilized due to its spatial shift sensitivity. In this paper, we address the problem of face recognition in controlled scenarios. Our focus is on the selection of a suitable signal representation and the development of a better strategy for face feature construction. We demonstrate that through our Block Matching scheme Gabor phase information is powerful enough to improve the performance of face identification. Compared to state of the art Gabor filtering based approaches, the proposed algorithm features much lower algorithmic complexity. This is mainly due to our Block Matching enables the employment of high definition Gabor phase. Thus, a single-scale Gabor frequency band is sufficient for discrimination. Furthermore, learning process is not involved in the facial feature construction, which avoids the risk of building a database-dependent algorithm. Benchmark evaluations show that the proposed learning-free algorithm outperforms state-of-the-art Gabor approaches and is even comparable to Deep Learning solutions.

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“…Since different texture categories and different coverage areas capture different aspects of grey-level variation characteristics of hand vein patterns and result in different classification performance, also proposed in this paper is score weighted fusion to yield an overall recognition performance that is higher than those based on individual texture categories. Although wavelet-based LBP has been previously proposed (Song and Li, 2013;Mu et al, 2011), this paper extends it to multi-radius wavelet LBP for image classification based on discriminative power of LBP computed at individual sampling radius in both spatial and wavelet domains. Furthermore, wavelet LBP has not been investigated for biometric recognition based on hand vein patterns.…”

Section: Introductionmentioning

confidence: 99%

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Wang

Duan

Shark

et al. 2016

IJCAT

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Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, Local Binary Patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.

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Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern

Cited by 41 publications

References 31 publications

How Good Can a Face Identifier Be Without Learning?

How Good Can a Face Identifier Be Without Learning?

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

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