Multimodal Recognition System Based on High-Resolution Palmprints

Hussein, Inass Shahadha; Sahibuddin, Shamsul; Nordin, Jan; Sjarif, Nilam Nur Binti Amir

doi:10.1109/access.2020.2982048

Cited by 8 publications

(9 citation statements)

References 46 publications

(44 reference statements)

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“…Template Matching Approach: This approach represents the global object image by using parts and the possibility for detection. [21], [61], [79], [80]. iv.…”

Section: B Region Of Interest (Roi) Extraction Methodsmentioning

confidence: 99%

Contactless Palmprint Recognition System: A Survey

et al. 2022

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Information systems in organizations traditionally require users to remember their secret pins (password), token, card number, or both to confirm their identities. However, the technological trend has been moving towards personal identification based on individual behavioural attributes (such as gaits, signature, and voice) or physiological (such as palmprint, fingerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identification, and it has received significant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing works, different types of "Region of Interest" (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the findings obtained are presented and discussed.INDEX TERMS biometrics, information system, palmprint images, region of interest

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“…Template Matching Approach: This approach represents the global object image by using parts and the possibility for detection. [21], [61], [79], [80]. iv.…”

Section: B Region Of Interest (Roi) Extraction Methodsmentioning

confidence: 99%

Contactless Palmprint Recognition System: A Survey

et al. 2022

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“…Local ridge pattern in small non overlapping patches (usually 8 × 8) is reconstructed by modelling it as a 2D sine wave [11,12,22,24,42]. Fourier analysis of a high-quality patch shows a pair of clear peaks corresponding to ridge pattern.…”

Section: Region Growing Techniquesmentioning

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 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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“…Filtering methods and wrapping methods are two categories into which function reduction algorithms can be divided. In order to analyze and choose subsets of features, the filtering techniques concentrate on the overall qualities of the data, excluding the chosen learning algorithm or classifier [27].…”

Section: Features Selectionmentioning

confidence: 99%

“…These properties are those of the input ear image. Utilizing featurelevel fusion, the feature vectors are fused into one long vector via concatenation [27]. The result of a possible combination of these features is 3822 features.…”

Section: Ear Local Features Extractionmentioning

confidence: 99%

Human Recognition based on Multi-instance Ear Scheme

Hussein,

Sjarif

2023

IJC

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Ear biometrics is one of the primary biometrics that is definitely standing out. Ear recognition enjoys special benefits and can make distinguishing proof safer and dependable along with other biometrics (for example fingerprints and face). Particularly as a supplement to face recognition schemes that experience issues in genuine circumstances. This is because of the extraordinary variety of a planar representation of a confusing object that is varied in shapes, illumination, and profile shape. This study is an endeavor to conquer these restrictions, by proposing scale-invariant feature transform (SIFT) calculation to extract feature vector descriptors from both left and right ears which is to be melded as one descriptor utilized for verification purposes. Likewise, another plan is proposed for the recognition stage, based on a genetic algorithm-backpropagation neural network as an accurate recognition approach. This approach will be tried by utilizing images from the Indian Institute of Technology Delhi's creation (IITD). The suggested system exhibits a 99.7% accuracy recognition rate.

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Multimodal Recognition System Based on High-Resolution Palmprints

Cited by 8 publications

References 46 publications

Contactless Palmprint Recognition System: A Survey

Contactless Palmprint Recognition System: A Survey

Palmprint enhancement network (PEN) for robust identification

Human Recognition based on Multi-instance Ear Scheme

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