Multi-Algorithm of Palmprint Recognition System Based on Fusion of Local Binary Pattern and Two-Dimensional Locality Preserving Projection

Ali, Mouad M. H.; Yannawar, Pravin; Gaikwad, Ashok T.

doi:10.1016/j.procs.2017.09.091

Cited by 24 publications

(14 citation statements)

References 31 publications

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“…Supervised linear method PCA + LDA [42] Kernel method Kernel PCA and kernel Fisher discriminant applications [43,44] Tensor method 2DPCA [13] 2D LPP [25] Transform the domain Subspace methods in the transform domains [42] Invariant moments: Zernike moments [45] and Hu Invariant moments [46]…”

Section: The Approaches Work Representation Subspace Methodsmentioning

confidence: 99%

“…Furthermore, the palmprint image has a unique feature as it will be unchanged for a long period of time [24,25]. Palmprint offers many distinctive features that can be used for precise recognition.…”

Section: Palmprint Technology Overviewmentioning

confidence: 99%

“…Kanchana and Balakrishnan [80] proposed a method of Rabin-Karp Palm-Print Pattern Matching (RPPM) to improve the matching accuracy of the palmprint features by using the double hash in RPPM. Ali et al [25] used a hybrid approach for palmprint identification, by using various algorithms such as local binary pattern (LBP), two-dimensional localized preserving projection (2DLPP) and the fusion of LBP + 2DLPP. Table 6 summarizes previous works based on the contactless database.…”

Section: B Review Based Contactless Palmprint Databasementioning

confidence: 99%

See 2 more Smart Citations

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

Hussein¹,

Sahibuddin²,

Sjarif³

2018

ijacsa

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Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases.

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Section: The Approaches Work Representation Subspace Methodsmentioning

confidence: 99%

Section: Palmprint Technology Overviewmentioning

confidence: 99%

Section: B Review Based Contactless Palmprint Databasementioning

confidence: 99%

See 1 more Smart Citation

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

Hussein¹,

Sahibuddin²,

Sjarif³

2018

ijacsa

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“…Biometrics techniques are modern and effective ways of achieving technological security goals. These techniques are interesting options for identifying humans depending on their biological features as an alternative to passwords, personal identification number (PINs), and smart cards, which depend on recall of a unique code [1], [2], [3] and [4]. Biometric techniques might be categorized into behavioral and physiological approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous techniques help detect ROI regarding the palm prints and have been proposed for improving the biometric system's performance. For instance: a study conducted by Mouad et al Suggested a method [1]. The main steps to obtain ROI in this method begin as palm images are converted to grayscale ones, enhancement, Binarization, and boundary detections; detection of reference points, valley point, extreme point, scaling and ultimately cropping ROI.…”

Section: Introductionmentioning

confidence: 99%

New Algorithm for Palm Print Detection

Qassir¹,

Hussain²,

Abbas³

2020

Journal of Southwest Jiaotong University

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Advances in technology are increasing the demand for the biometric technology of palm print detection. This work presents a region of interest segmentation algorithm for palm prints based on Hue Saturation Brightness. The aim of this work was to detect palm print region of interest based on skin color and geometric features. After a color palm image is read as a JPEG file, the algorithm passes through three main stages of preprocessing: palm localization, region of interest localization and region of interest extraction. The preprocessing stage consists of three steps: conversion to Hue Saturation Brightness, skin color modeling, and de-noising. The second main stage of palm localization consists of clipping from the left, from the right, from the top and from bottom. The third stage consists of the two steps of determining and extracting region of interest. The algorithm was tested on a dataset of 180 palm images from the Institute of Automation Chinese Academy of Sciences dataset, which contains an equal number of right-hand and left-hand palm images. The results showed that the Hue Saturation Brightness color space for skin detection was superior to other recognition methods, with an accuracy rate of 98.8%.

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Robust features fusion utilization for supervised palmprint recognition

Ata

Elgamily²,

Mohamed

2022

Concurrency and Computation

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This article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been appliedfor the resultant ROI including (PCA), transformed domain, and invariant moments.Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition. K E Y W O R D Sdiscrete cosine transform, discrete wavelet transform, invariant moments, principal component analysis, Hough transform, morphological features, transformed domain.

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Multi-Algorithm of Palmprint Recognition System Based on Fusion of Local Binary Pattern and Two-Dimensional Locality Preserving Projection

Cited by 24 publications

References 31 publications

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

The Fundamentals of Unimodal Palmprint Authentication based on a Biometric System: A Review

New Algorithm for Palm Print Detection

Robust features fusion utilization for supervised palmprint recognition

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