Bimodal biometric system based on SIFT descriptors of hand images

Charfi, Nesrine; Trichili, Hanène; Alimi, Adel M.; Solaiman, Basel

doi:10.1109/smc.2014.6974586

Cited by 19 publications

(3 citation statements)

References 20 publications

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“…In their turn in [34], they proposed a new verification system on a basis of palmprint and hand shape fusion screened through SIFT. Such an adjustment improved the effectiveness of SIFT in extracting the features that are invariant to scaling and image rotation across various applications such as object identification and video tracking.…”

Section: Related Workmentioning

confidence: 99%

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

Elgallad¹,

Ouarda²,

Alimi³

2019

IJACSA

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Biometric recognition or biometrics has emerged as the best solution for criminal identification and access control applications where resources or information need to be protected from unauthorized access. Biometric traits such as fingerprint, face, palmprint, iris, and hand-geometry have been well explored; and matured approaches are available in order to perform personal identification. The work emphasizes the opportunities for obtaining texture information from a palmprint on the basis of such descriptors as Curvelet, Wavelet, Wave Atom, SIFT, Gabor, LBP, and AlexNet. The key contribution is the application of mode voting method for accurate identification of a person at the fusion decision level. The proposed approach was tested in a number of experiments at the CASIA and IITD palmprint databases. The testing yielded positive results supporting the utilization of the described voting technique for human recognition purposes.

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Section: Related Workmentioning

confidence: 99%

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

Elgallad¹,

Ouarda²,

Alimi³

2019

IJACSA

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“…Charfi et al presented a new approach for palmprint recognition where they combined hand shape and palmprint biometric modalities extracted using the Scale Invariant Feature Transform (SIFT). In their experiment, they used the Indian Institute of Technology Delhi (IITD) dataset containing 1150 left hand and palmprint images collected from 230 users, to evaluate their SIFT approach.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

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The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

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“…These hand multibiometric approaches include hand geometry and palmveins [140], palmprint and palmveins [185,136,59,182], palmprint and hand geometry [153,103,137,141,56,110,20], hand veins, hand geometry and fingerprints [168], palmprint, hand geometry and fingerprints [7,53,197], palmprint, hand geometry and hand veins [71,14], palmprint and knucleprint [120,130] or hand geometry, palmprint, knuckleprint, palm veins and finger veins [135].…”

Section: Introductionmentioning

confidence: 99%

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

Sánchez¹

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In this thesis different techniques of image processing, machine learning and information fusion have been analysed in relation to their applicability to contact-less hand biometrics. To this end, a modular and configurable system that explodes the multimodal nature of the hand to increase its robustness and accuracy have been designed, implemented and evaluated. Given the fact that different applications have different accuracy and time performance needs, the evaluation is aimed to provide a fair comparative of methods under different environmental conditions that helps to adapt the system to the specific requirements of a concrete final application.A correct hand segmentation is necessary to extract reliable and invariant biometric features. For this reason, a comparative of different segmentation methods that include well-known methods such as global thresholding and graph cuts as well as a novelty flooding-based method which combines different image-based segmentation approaches. These methods have been compared using diverse datasets of images which cover a wide spectrum of capturing conditions.On the other hand, a comprehensive evaluation of different palmprint feature extraction methods comprising Gabor and Sobel filters, Local Binary Patterns, Local Derivative Patterns and Curvelets has been carried out. Different parameter configurations have also been tested with the aim of finding out which arrangement provides better results for each method. In addition to palmprint, also hand geometry features have been extracted. This evaluation includes also two different feature matching approaches: distance-based and Support Vector Machines.In addition, it has also been evaluated the feasibility of combining different feature extraction methods to yield into a more precise and robust multimodal solution. Two different levels for fusing the biometric information have been compared: score-level and feature-level.Finally, an evaluation methodology that allows for a fair comparison between different methods has been proposed. In particular, an evaluation protocol is offered with the aim of not only obtaining an extensive evaluation of the complete system under different environmental conditions, and testing multiple combinations of methods for each module, but also providing a basis against which to compare future research.Keywords: Accuracy, Biometrics, Computation Requirements, Configurable, Curvelets, Distance, Evaluation, Feature-level fusion, Flooding, Fusion, Gabor, Graph-Cuts, Hand Geometry, Local Binary Patterns, Local Derivative Patterns, Modular, Multimodal, Palmprint, Score-level fusion, Segmentation, Sobel, Support Vector Machines, Thresholding, Varied Environmental Conditions. R E S U M E NEn esta tesis se han analizado diferentes técnicas de procesado de imagen, aprendizaje automático y fusión de la información en relación a su aplicabilidad a la biometría de mano sin contacto. Para ello se ha diseñado, implementado y evaluado un sistema modular y configurable que explota la naturaleza multimodal de la...

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Bimodal biometric system based on SIFT descriptors of hand images

Cited by 19 publications

References 20 publications

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

Dense Hand-CNN: A Novel CNN Architecture based on Later Fusion of Neural and Wavelet Features for Identity Recognition

State of the art and perspectives on traditional and emerging biometrics: A survey

Analysis of image processing, machine learning and information fusion techniques for contact-less hand biometrics

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