A secure and robust indexing algorithm for distorted fingerprints and latent palmprints

Khodadoust, Javad; Medina‐Pérez, Miguel Angel; Loyola‐González, Octavio; Monroy, Raúl; Khodadoust, Ali Mohammad

doi:10.1016/j.eswa.2022.117806

Cited by 10 publications

(5 citation statements)

References 53 publications

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“…This value of EER is impressive because it comes without making any improvement to minutiae encoding and matching algorithm. And by only dropping Dai and Zhou [11] Region growing Gabor filters 2.99 Tariq et al [44] Gradient based Gabor filters 0.38 Ghafoor et al [19] Gradient based Gabor filters 0.18 Hussain et al [22] Region growing Gabor filters 0.04 Khodadoust et al [27] Curved 2).…”

Section: Matching Resultsmentioning

confidence: 99%

“…Their method does not incorporate enhancement of palmprints. Apart from that, recent research on palmprints [9,11,12,17,19,22,27,33,42] has focussed more on palmprint matching rather than enhancement, ergo limited novelty has been introduced in palmprint enhancement process. To the best of our knowledge, no approach has been proposed for the specific problem of converting a palmprint directly into its corresponding enhanced version using CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…Palmprint based systems along with fingerprints were leader in biometric industry in the last decade and palmprint based systems are expected to show a CAGR of 18.28% and touch around $2.6 Billion by 2030 [40]. As a result of growing popularity of palmprints, research community's interest in palmprints has also seen sharp increase [3,15,27,32,33]. Palmprints are an attractive biometric as they provide identification features at multiple image resolutions which are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Palmprint enhancement network (PEN) for robust identification

Mehmood

Taj

Ghafoor

2023

Multimed Tools Appl

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Despite being reliable, palmprints have not received as much attention as other biometrics such as fingerprints, face or iris. Amount of information provided by high resolution palmprints and the fact that they have huge forensic value makes them a preferred biometric choice for large scale identification systems. In palmprints, extraction of reliable features for identification is still a challenging task especially because most of palmprints found in the real world, e.g., in crime scenes, are of poor quality. This makes palmprint enhancement a crucial pre cursor to identification. Errors during enhancement result in extraction of un-reliable features which deteriorate identification accuracy. Recent works in palmprints have focused more on matching algorithms and limited novelty has been introduced in enhancement. Enhancement techniques used on high resolution palmprints recently are either borrowed from fingerprint techniques or are built on the high-risk assumption that palm ridge pattern is stationary or smooth in a local area. Large size and abruptly changing ridge pattern of palmprints dictates the need for a more robust enhancement scheme. This paper proposes a novel deep learning based high resolution palmprint enhancement approach that is able to process large areas of palmprint without making the assumption that underlying ridge pattern is stationary. We have tested proposed enhancement approach on a renowned high resolution palmprint dataset which shows that proposed technique performs favourably in comparison to state of the art.

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Section: Matching Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Palmprint enhancement network (PEN) for robust identification

Mehmood

Taj

Ghafoor

2023

Multimed Tools Appl

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“…Different security and recognition measures are deployed on the palmprint images to safely store and retrieve the palmprint for authenticating users. As presented in [28], a secured mechanism for indexing palmprint and fingerprints with many distortion points. The authors aimed to prevent unauthorized users from accessing the palmprint by converting the unprotected template to a protected one.…”

Section: Related Workmentioning

confidence: 99%

Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

Ezz¹,

Alanazi²,

Mostafa³

et al. 2023

Computer Systems Science and Engineering

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Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves robust and reliable end-to-end Palmprint authentication by extracting the convolutional features using VGG-16 Palmprint and the similarity of two input Palmprint using the Siamese network. The second stage uses the CASIA dataset to train and test the Siamese network. The suggested model outperforms comparable studies based on the deep learning approach achieving accuracy and EER of 91.8% and 0.082%, respectively, on the CASIA left-hand images and accuracy and EER of 91.7% and 0.084, respectively, on the CASIA right-hand images.

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“…The efficiency of recognizing users' Palmprint can be enhanced using balanced loss and contrast loss, and experimental results are conducted on seven different datasets to provide provable palmprint security measures. The authors of [25] proposed a fingerprint and palmprint recognition filtering mechanism for biometric template protection that can protect biometric data from illegal users. A feature called the middle of the triangle is applied by validating the input image quality then the palmprint details or minutiae are extracted from the model to increase the recognition accuracy.…”

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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A secure and robust indexing algorithm for distorted fingerprints and latent palmprints

Cited by 10 publications

References 53 publications

Palmprint enhancement network (PEN) for robust identification

Palmprint enhancement network (PEN) for robust identification

Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

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

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