Palmprint recognition system based on deep region of interest features with the aid of hybrid approach

Türk, Ömer; Çalışkan, Abidin; Acar, Emrullah; Ergen, Burhan

doi:10.1007/s11760-023-02612-0

Cited by 3 publications

(2 citation statements)

References 55 publications

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“…Türk et al (2023) [86] devised a hybrid palmprint recognition framework fusing deep learning and classical machine learning methodologies. Their processing pipeline starts with multiple preprocessing interventions encompassing boundary delineation, binarization, finger exclusion, edge contour extraction, noise filtering, and image thinning to maximize distinctive friction ridge information.…”

Section: Deep Learning-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: Deep Learning-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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“…REST API was used to interact with Google's cloud vision platform. The real-time implementation of the hardware as well as software solution were implemented and executed on a Raspberry Pi 3 model B+ board with Pi Camera module.This paper tries to tackle such limitations presented by [27][28][29][30][31][32], [2], [10] by making use of raspberry pi and faster object detection and classification technique to improve video surveillance system III PROPOSED DESIGN AND METHODOLOGY This section describes in detail, the methodological steps employed in the design and implementation of the proposed intelligent surveillance system. Hence, the proposed system utilized flow design tool, flowchart, block diagram and circuit diagram for the implementation to describe the technical phase of the methodology.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Video Surveillance System Using Faster Regional Convolutional Neural Networks

OLANİYİ,

GANİYU

2023

Balkan Journal of Electrical and Computer Engineering

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Over the years, the need for intelligent video surveillance has increased in other to enhance security and safety in the society. Government, private organization and individual need to make sure its properties are kept safe from intruders and as such intelligent video surveillance plays a key role in ensuring that this is achieved. Intelligent video surveillance is embedded with the capability of providing real time intelligent surveillance and also automatically provide analysis of video and image data without human operation. In the development of such systems, computer vision, machine learning and deep learning plays a vital role in achieving this. Therefore, this paper provides a general survey on intelligent video surveillance system, overview of background concept and discussion on object detections and classification, tracking and deep networks. Also, this paper presents an efficient and faster object detection and classification techniques for intelligent video surveillance.

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Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

Alshakree,

Akbas,

Rahebi

2023

SIViP

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Palmprint recognition system based on deep region of interest features with the aid of hybrid approach

Cited by 3 publications

References 55 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

Intelligent Video Surveillance System Using Faster Regional Convolutional Neural Networks

Human identification using palm print images based on deep learning methods and gray wolf optimization algorithm

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