Multispectral palmprint recognition: A state-of-the-art review

Aberni, Yassir; Boubchir, Larbi; Daachi, B.

doi:10.1109/tsp.2017.8076097

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…A deep learning algorithm is applied to extract discriminative features of the Palmprint images that can be used for verification and identification. As presented in [17], the verification of Palmprint images can be enhanced using multispectral images that can increase reliability and efficiency. Multispectral recognition can be applied through different approaches such as structured, appearance, statistical, coding, and hybrid methods.…”

Section: Related Workmentioning

confidence: 99%

Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection

Hamouda¹,

Ezz²,

Mostafa³

et al. 2023

Computer Systems Science and Engineering

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Many types of research focus on utilizing Palmprint recognition in user identification and authentication. The Palmprint is one of biometric authentication (something you are) invariable during a person's life and needs careful protection during enrollment into different biometric authentication systems. Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification. This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users. The HAMTE is generated for each user during the enrollment phase, which is responsible for generating a secure template for the enrolled user. The proposed network secures the person's Palmprint template by translating it into an irreversible template (different features space). It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person's template from being stolen. Experimental results are conducted on the CASIA database, where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates. The recognition accuracy deviated by around 3%, and the equal error rate (EER) by approximately 0.02 compared to the original data, with appropriate performance (approximately 13 ms) while preserving the irreversibility property of the secure template. Moreover, the brute-force attack has been analyzed under the new Palmprint protection scheme.

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Section: Related Workmentioning

confidence: 99%

Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection

Hamouda¹,

Ezz²,

Mostafa³

et al. 2023

Computer Systems Science and Engineering

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“…One can find a multispectral (MSP) based palmprint representation that provides additional discriminating information. Because four spectral bands have peaks at various light wavelengths, such as vein networks where the nearinfrared band (NIR) penetrates the skin, visualisation of the vein pattern is allowed [7]. Therefore, hyperspectralbased palmprint imaging is a new research area in which researchers in numerous domains have been interested because of its high ability to distinguish between customers (users).…”

Section: Introductionmentioning

confidence: 99%

An effective hyperspectral palmprint identification system based on deep learning and band selection approach

Korichi,

Samai,

Benlamoudi

et al. 2023

IJCAI

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Over the past two decades, biometric technologies have exploded because anything that identifies a person provides a source of information. The palmprint modality is a biometric characteristic of great interest to researchers, and its traits can be found in various representations, including grayscale, colour, and multi/hyperspectral representations. The most difficult challenge in developing a hyperspectral palmprintbased recognition system is determining how to use all the information available in these spectral bands. In this paper, we propose a hyperspectral palmprint identification system. In the first stage, an Optimal Clustering Framework (OCF) is proposed to extract the most representative bands. Then, two types of feature extraction methods (handcrafted and deep learning approaches) were used to determine the best method to describe palmprint features. After setting the number of selected bands to 4, we performed our set of experiments using the Hong Kong Polytechnic University (Poly U), which consists of 69 spectral bands. The results indicated that the proposed system offers the best performance, which qualifies it to be intended for usage in high-security situations.Povzetek:glavni cilj tega dela je oblikovati biometrični identifikacijski sistem z uporabo hiperspektralnega slikanja odtisa dlani (HSP) in nove sheme izbire pasu.Informatica 47 (2023) 63-80 M. Korichi et al.

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“…So this system is suitable when applied at the airport. Outline researchers in the field of palmprint recognition have divided the research subsection into four parts, namely preprocessing, homogeneity of image position, dimension reduction, and matching techniques [1]- [3]. If the research emphasizes the search for characteristics in the form of the main line of the palm, then the acquisition tool to get a digital image is enough to use a camera or web camera.…”

Section: Introductionmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Kusban

2022

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Various methods are used to obtain a superior palmprint recognition system. After selecting a palmprint image filter, using Gabor orientation scale pairs is an option to support the refinement of the verification process. Many researchers use the [8 × 5] pair for the value of the Gabor orientation scale in the field of palmprint recognition. However, from the experiments conducted, other Gabor pairs have more impact on system improvement. The problem is to get the most suitable value pairs for palmprint applications, so in this study, a comparison of seven kinds of Gabor pairs is carried out. This Gabor pair being compared applies using original images, PCA dimension reduction, and the Euclidean method. From the research that has been done, the pair of Gabor orientation scale [8 × 7] or image expansion of 56 will have the most significant impact compared to other pairs. Suppose the result of this Gabor pair is [8 × 7] by using other improvement systems, namely the 3W filter instead of the original image, KPCA to replace the PCA, and the cosine method in the matching method. In that case, it will increase the verification value by 99.611%. The trial value obtained can be an alternative method of choice for improving palmprint recognition.

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Multispectral palmprint recognition: A state-of-the-art review

Cited by 11 publications

References 32 publications

Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection

Innovative Hetero-Associative Memory Encoder (HAMTE) for Palmprint Template Protection

An effective hyperspectral palmprint identification system based on deep learning and band selection approach

International Journal of Electronics and Telecommunications

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