Selection of discriminative sub-regions for palmprint recognition

Hammami, Mohamed; Jemaa, Salma Ben; Ben-Abdallah, Hanêne

doi:10.1007/s11042-012-1109-x

Cited by 25 publications

(7 citation statements)

References 30 publications

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“…Hammami et al (2014) [69] employed a technique involving the division of the complete palmprint image into smaller sub-regions. Within each of these sub-regions, they applied the local binary pattern (LBP) operator to capture the texture characteristics.…”

Section: Texture-based Approachesmentioning

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: Texture-based Approachesmentioning

confidence: 99%

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

Amrouni,

Benzaoui,

Zeroual

2023

Applied Sciences

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“…A second category of approaches defines the contour of the extracted hand, and the distance from a point of reference (the geometric center [18], [35] or the wrist [36], etc) to the pixels found on the contour [20], [37], [38], [39], [40], [41], [42], [43]. Considering this distribution of distances, the peaks generally correspond to the tips of the fingers, while the local minimas correspond to the finger valleys.…”

Section: A Standard Palmprint Roi Extractionmentioning

confidence: 99%

Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review

Ungureanu,

Salahuddin,

Corcoran

2020

Preprint

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As a biometric palmprints have been largely underutilized, but they offer some advantages over fingerprints and facial biometrics. Recent improvements in imaging capabilities on handheld and wearable consumer devices have re-awakened interest in the use fo palmprints. The aim of this paper is to provide a comprehensive review of state-of-the-art methods for palmprint recognition including Region of Interest extraction methods, feature extraction approaches and matching algorithms along with overview of available palmprint datasets in order to understand the latest trends and research dynamics in the palmprint recognition field.

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“…In our work, these two "landmarks" points F 1 and F 2 are defined differently depending on the hand size (cf. [24]). …”

Section: ) Palmprint and Fingerprint Surface Rois Extractionmentioning

confidence: 99%

“…Thus, we have applied the SFFS (Sequential Forward Floating Search) algorithm as a feature selection method to select the sub-regions having the most discriminating features for recognition instead of using the whole palmprint image as input for the recognition algorithm. After a series of experiments, the SFFS algorithm selects 24 sub-regions for the left palm and 19 sub-regions for the right palm [24].…”

Section: Feature Extractionmentioning

confidence: 99%

Human Identification Based on the Palmar Surface of the Hand

Jemaa

Hammami

2016

2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)

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In this paper, we investigate a new multimodal biometric approach for personal recognition, based on two different biometrics computed from the same hand image: palmprint and fingerprint surface. The detection of these modalities is conducted with no constraints imposed on the subject. In addition, this approach has the particularity of using a new fusion scheme based on the rank level integration method to consolidate the results obtained from the different biometric instances. Rank level fusion is a relatively new fusion approach. It is not a well-studied research problem, yet it has a high potential for efficient consolidation of multiple unimodal biometric outputs matching. To the best of our knowledge, we present the first application of the rank level fusion to consolidate the rank information produced by the five individual fingerprint surface and the palmprint. The achieved experimental results have shown that the designed system achieves an excellent recognition rate and provides more security than unimodal biometric system.

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Selection of discriminative sub-regions for palmprint recognition

Cited by 25 publications

References 30 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

Towards Unconstrained Palmprint Recognition on Consumer Devices: a Literature Review

Human Identification Based on the Palmar Surface of the Hand

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