Palmprint based recognition system using phase-difference information

Badrinath, G. S.; Gupta, Phalguni

doi:10.1016/j.future.2010.11.029

Cited by 54 publications

(27 citation statements)

References 43 publications

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“…The ROI extraction phase plays an important role in palmprint verification, since the accuracy of the whole process is highly dependent on ROI extraction, where several findings have been discussed in previous work [14][15][16][17][18][19][20][21][22][23][24][25]. In [18], ROI extraction steps consist of (i) skin-color thresholding, (ii) hand valley detection, and (iii) finding palm region.…”

Section: Roi Extractionmentioning

confidence: 99%

“…Finally, based on the coordinate system, a fixed-size sub-image was extracted. Similarly, the methods of Connie [15], Han [16], and Badrinath [17] used the finger gaps to extract the ROI. Although these methods are famous and widely used, they depend on the gaps between the fingers as reference points to determine the coordinate system, which means that all fingers must be spread and the hand should be facing toward the camera.…”

Section: Roi Extractionmentioning

confidence: 99%

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Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

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Section: Roi Extractionmentioning

confidence: 99%

Section: Roi Extractionmentioning

confidence: 99%

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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“…Further, fisher linear discrimination is applied for dimension reduction 15 . Recent researches are focused on improving the quality of palmprint features against variations in position, rotation, scale, noise, translation, occlusion, and illumination 4,15,19,20 . To make a robust palmprint recognition system, two or more feature extraction techniques are essentially combined 15 .…”

Section: Literature Reviewmentioning

confidence: 99%

Occlusion Invariant Palmprint Recognition with ULBP Histograms

Tamrakar

Khanna

2015

Procedia Computer Science

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This work presents a palmprint recognition approach based on the local distribution of uniform patterns. Preprocessing segments palmprint region from foreground of image as well as aligns the palmprint along the line between tips of middle finger and mid of wrist. Palmprint texture has line and wrinkles like features which are a type of uniform patterns. Uniform LBP (ULBP) refers to the pattern of uniform appearance with limited discontinuities. ULBP histograms are used as features to handle occlusion upto 36%. Experiments are performed on 400 classes of the PolyU 2D, 600 classes of CASIA and 150 classes of IIITDMJ palmprint database. Correct identification rates of 99.6% for normal palmprints and 99% for randomly occluded palmprints are obtained on PolyU 2D database.

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“…Hand image has been acquired with the help of a low cost flat bed scanner. Desired area of palm, termed as palmprint or region of interest (ROI), has been extracted from the hand image and subsequently enhanced to improve its texture as described in [4]. The enhanced palmprint is segmented into oriented overlapping rectangular blocks and features are extracted using the difference of 1D-DCT coefficients of adjacent blocks.…”

Section: Proposed Systemmentioning

confidence: 99%

An Efficient Palmprint Based Recognition System Using 1D-DCT Features

Badrinath

Tiwari

Gupta

2012

Lecture Notes in Computer Science

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Abstract. This paper makes use of one dimensional Discrete Cosine Transform (DCT) to design an efficient palmprint based recognition system. It extracts the palmprint from the hand images which are acquired using a flat bed scanner at low resolution. It uses new techniques to correct the non-uniform brightness of the palmprint and to extract features using difference of 1D-DCT coefficients of overlapping rectangular blocks of variable size and variable orientation. Features of two palmprints are matched using Hamming distance while nearest neighbor approach is used for classification. The system has been tested on three databases, viz. IITK, CASIA and PolyU databases and is found to be better than the well known palmprint systems.

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Palmprint based recognition system using phase-difference information

Cited by 54 publications

References 43 publications

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Occlusion Invariant Palmprint Recognition with ULBP Histograms

An Efficient Palmprint Based Recognition System Using 1D-DCT Features

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