Palmprint recognition using state‐of‐the‐art local texture descriptors: a comparative study

Idrissi, Abdellatif El; Merabet, Youssef El; Ruichek, Yassine

doi:10.1049/iet-bmt.2019.0103

Cited by 14 publications

(6 citation statements)

References 83 publications

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“…In this work, the feature vectors generated using BSIF have 56 dimensions. An interesting study evaluating the robustness of 27 descriptors in palmprint recognition [53] describes that the BSIF descriptor was among the Top-3 best descriptors evaluated.…”

Section: Binarized Statistical Image Features (Bsif)mentioning

confidence: 99%

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Pereira

Bertolini

Teixeira

et al. 2020

Computer Methods and Programs in Biomedicine

435

302

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Background and Objective:The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. Methods:In order to achieve the objectives, we have proposed a classification schema considering the following perspectives: i) a multi-class classification; ii) hierarchical classification, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in the schema in order to re-balance the classes distribution. We observed that, texture is one of the main visual attributes of CXR images, our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in the schema in order to leverage the strength of multiple texture descriptors and base classifiers at once.To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others. Results:The proposed approach tested in RYDLS-20 achieved a macro-avg F1-Score of 0.65 using a multiclass approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario. Conclusions:As far as we know, the top identification rate obtained in this paper is the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here.

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Section: Binarized Statistical Image Features (Bsif)mentioning

confidence: 99%

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

Pereira

Bertolini

Teixeira

et al. 2020

Computer Methods and Programs in Biomedicine

435

302

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“…As a result of the development of biometric identification technology, biological traits provide major benefits, such as passwords, and human biometric characteristics for identity authentication have attracted considerable attention. Biometric identification mainly includes iris recognition [ 1 , 2 , 3 ], facial recognition [ 4 ], voice recognition [ 5 ], retina recognition [ 6 ], palm print recognition [ 7 ], vein recognition [ 8 , 9 ], fingerprint recognition [ 10 , 11 , 12 ], and so on. Biometric technology has vast applicability and business potential.…”

Section: Introductionmentioning

confidence: 99%

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Niu

et al. 2023

Micromachines

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Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

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“…The local texture descriptors‐based methods code the local structural for each pixel, and compute the histogram of the local region [17]. Jia et al.…”

Section: Introductionmentioning

confidence: 99%

“…The local texture descriptors-based methods code the local structural for each pixel, and compute the histogram of the local region [17]. Jia et al [18] designed a histogram of oriented lines (HOL) descriptor as a variant of histogram of oriented gradients (HOG).…”

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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Palmprint recognition using state‐of‐the‐art local texture descriptors: a comparative study

Cited by 14 publications

References 83 publications

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

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

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