Multi-orientation and multi-scale features discriminant learning for palmprint recognition

Ma, Fei; Zhu, Xiaoke; Wang, Cailing; Liu, Huajun; Jing, Xiao‐Yuan

doi:10.1016/j.neucom.2018.06.086

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…An ab-initio linear non-supervised approach called principal component analysis (PCA) was applied to extract the holistic vectors. [77], [87], [88], while various unsupervised approaches, such as independent component analysis (ICA) and locality preserving projection (LPP), have been used to recognize palmprints [10], [24], [53]. However, supervised methods are generally more efficient when resolving issues with recognition.…”

Section: Holistic-based Feature Extractionmentioning

confidence: 99%

“…Holistic Feature Extraction A. Subspace Method [50], [64], [66], [77][27], [95] -Unsupervised linear method Application of PCA and other unsupervised subspace methods [70] -Supervised linear method PCA+LDA on raw data [24], [93] -Kernel method Applications of kernel PCA and kernel fisher discriminant [96], [54] Transform domain subspace method Subspace methods in the transform domains [6], [8] B. Invariant Moment Zernike moments And Hu Invariant moment [82] C. Spectral Representation Wavelet Signature Global statistical signatures in the wavelet domain [25], [87] Correlation filter Advanced correlation filter Classifier Design [16] The subspace method's performance can be enhanced further by using the image transform. After this, the transform coefficients may be effectively used to recognize palmprint and robust variability within the class.…”

Section: Reference Approach Descriptionmentioning

confidence: 99%

“…The performance of the system is due to many unavoidable factors, e.g. translation, rotation, and deformation of the palmprint images, which is unsatisfactory [87] proposed dilation of template line image before matching and [6] [48] iris code has a coding-based method with a lower memory requirement, and quick matching speed was effective in the representation and matching of palmprints. It should be noted that a palm code method which encodes as bitwise functions the filter response phase and convolved palmprint image with a 2D Gabor filter [25], [73].…”

Section: Reference Approach Descriptionmentioning

confidence: 99%

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Contactless Palmprint Recognition System: A Survey

et al. 2022

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Information systems in organizations traditionally require users to remember their secret pins (password), token, card number, or both to confirm their identities. However, the technological trend has been moving towards personal identification based on individual behavioural attributes (such as gaits, signature, and voice) or physiological (such as palmprint, fingerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identification, and it has received significant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing works, different types of "Region of Interest" (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the findings obtained are presented and discussed.INDEX TERMS biometrics, information system, palmprint images, region of interest

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Section: Holistic-based Feature Extractionmentioning

confidence: 99%

Section: Reference Approach Descriptionmentioning

confidence: 99%

Section: Reference Approach Descriptionmentioning

confidence: 99%

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Contactless Palmprint Recognition System: A Survey

et al. 2022

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“…Ma et al. [25] introduced the Fisher criterion into palmprint coding, and proposed the discriminant orientation and scale features learning (DOSFL) method. In Refs.…”

Section: Introductionmentioning

confidence: 99%

Palmprint recognition based on the line feature local tri‐directional patterns

Wang

Liu

et al. 2022

IET Biometrics

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Recent researches have shown that the texture descriptor local tri‐directional patterns (LTriDP) performs well in many recognition tasks. However, LTriDP cannot effectively describe the structure of palm lines, which results in poor palmprint recognition. To overcome this issue, this work proposes a modified version of LTriDP, called line feature local tri‐directional patterns (LFLTriDP), which takes into account the texture features of the palmprint. First, since palmprints contain rich lines, the line features of palmprint images, including orientation and magnitude, are extracted. The line features are more robust to variations compared to the original grayscale values. Then, the directional features are encoded as tri‐directional patterns. The tri‐directional patterns reflect the direction changes in the local area. Finally, the LFLTriDP features are constructed by the tri‐directional patterns, orientation and magnitude features. The LFLTriDP features effectively describe the structure of palm lines. Considering that most palm lines are curved, we use the concavity as supplementary information. The concavity of each pixel is obtained using the Banana filter and all pixels are grouped into two categories. The LFLTriDP features are refined to generate two feature vectors by the concavity to enhance the discriminability. The matching scores of the two feature vectors are weighted differently in the matching stage to reduce intra‐class distance and increase inter‐class distance. Experiments on PolyU, PolyU Multi‐spectral, Tongji, CASIA and IITD palmprint databases show that LFLTriDP achieves promising recognition performance.

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“…Thus, the principal line based method is able to provide stable performance for palmprint verification. Palmprint principal lines can be extracted by using the Gabor filter, Sobel operation, or morphological operation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

Pratyasha

Swarnkar

Padhy

2020

Preprint

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In this advanced decade, automatic identification of individuals is a significant achievement due to the high demand of security system. Hence, individual recognition using biometrics data is leading in the field of image processing. Although biometrics data analysis using thumb impression and finger-prints are very popular since many years, sometimes it leads to false acceptance and rejection if any physical change occurs in the finger ridges. There may be a high risk of hacking the biometrics data which is now a big challenge for cyber security employees. This paper captures the palm-print images of individuals as referred biometrics data for individual recognition. The research work is based on one of the prior issue that is feature extraction to extract the features of palm-print image such as principle lines, textures, ridges and pores etc. For this, some of the feature extraction techniques such as Derivatives of Gaussian filter (DoG), Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and competitive coding. Two types of filters: Gaussian Filter and Gabor filter are combined with each of the feature extraction scheme for the matching of sampled image with testing image. In the result, the error rates of each of the feature extraction algorithms are compared to recognize the palm image of two different individuals.

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Multi-orientation and multi-scale features discriminant learning for palmprint recognition

Cited by 21 publications

References 32 publications

Contactless Palmprint Recognition System: A Survey

Contactless Palmprint Recognition System: A Survey

Palmprint recognition based on the line feature local tri‐directional patterns

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

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