Occlusion Invariant Palmprint Recognition with ULBP Histograms

Tamrakar, Deepti; Khanna, Pritee

doi:10.1016/j.procs.2015.06.056

Cited by 19 publications

(3 citation statements)

References 19 publications

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“…The palmprint images within the Indian Institute of Information Technology, Design and Manufacturing, Jabalpur (IIITDMJ) [29] database were notably affected by palm movement and distortion. This database comprises 900 gray-scale palmprint images, sourced from 75 IIITDM Jabalpur students, with six images captured per palm during a single session.…”

Section: Iiitdmj (2015)mentioning

confidence: 99%

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

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This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research.

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Section: Iiitdmj (2015)mentioning

confidence: 99%

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

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“…Deepti Tamrakar and Pritee Khanna [20] has proposed Competitive coding method for feature extraction and then KNN Classifier is used for palmprint verification. Deepti Tamrakar and Pritee Khanna [21] presents a palmprint recognition approach based on the local distribution of uniform patterns where, ULBP histograms are considered as features and are able to handle occlusion up to 36%. Badrinath G. S. [22] proposes a palmprint based verification system using low-order Zernike moments of palmprint sub-images.…”

Section: Image Capture Feature Extractionmentioning

confidence: 99%

Palmprint Recognition System Robust to Occlusion using Gabor with 2D-2DPCA

Kaur¹,

Rai²

2017

IJCA

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Biometric based identification is one of the fields emerging recently, used as a form of identification and access control in various places to ensure security. Palmprint is one of the most unique and stable biometric characteristics. A palmprint contains different elements, including essential lines, wrinkles, edges, particulars focuses, solitary focuses and surface. It is essential to extract only useful segment of the palmprint image in the form of Region of Interest (ROI), which has highest concentration of potential features. It helps the system by reducing the size of template, speeding up the process and adds accuracy. Thus, palmprint recognition system's performance can be improved when size of the palm images can be reduced first and then features are extracted from the images to recognize the identity of an individual. This paper presents a texture based palm print recognition method which employ 2D Gabor filter to extract texture information from the central part of hand and use Twodirectional Two-dimensional Principal component analysis (2D-2DPCA) methods for dimension reduction. The test and training images are compared in terms of calculating Euclidean distance between them using KNN classifier. The proposed system is also robust to occlusion and can verify the user by comparing features of from nonoccluded region. All tests are performed on 100 classes of the Hong Kong PolyU palmprint database. The Hong Kong PolyU database comprises of 7960 pictures captured from 199 people, 398 palms. It comprises of 20 pictures of each palm and it is the biggest palmprint database freely accessible.

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“…Palmprint recognition system using VR-LBP and KAZE features for better … (Noor Aldeen Abbas Khalid) 1061 (DLBP), center-symmetric local binary pattern (CS-LBP), local derivative pattern (LDP), and completed local binary pattern (CLBP) [5]. Sehgal [7] proposed a palmprint recognition system using LBP and support vector machines (SVM), while Tamrakar and Khanna [8] presented an approach to palmprint identification based on local uniform LBP (ULBP) distribution. ULBP applies to the pattern with minimal discontinuities of uniform appearance.…”

Section: Introductionmentioning

confidence: 99%

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

A. Khalid,

Imran Ahmad,

Shie Chow

et al. 2024

Bulletin EEI

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The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

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Occlusion Invariant Palmprint Recognition with ULBP Histograms

Cited by 19 publications

References 19 publications

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions

Palmprint Recognition System Robust to Occlusion using Gabor with 2D-2DPCA

Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

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