Histogram of Oriented Lines for Palmprint Recognition

Jia, Wei; Hu, Rong-Xiang; Lei, Yingke; Zhao, Yang; Gui, Jie

doi:10.1109/tsmc.2013.2258010

Cited by 159 publications

(90 citation statements)

References 34 publications

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“…Recognition Accuracy Local binary pattern (LBP) 82.3% EigenPalm (EP) [18] 91.25% FisherPalm (FP) [18] 92.32% Image level fusion by PCA [17] 95.17% Gabor-based RCM (GRCM) [18] 96% Enhanced GRCM (EGRCM) [18] 98% Quaternion PCA [17] 98.13% PCA on HOG [19] 98.73% KPCA on HOG [19] 98.77% Quaternion PCA+Quaternion DWT [17] 98.83% LDA on HOG [19] 99.17% KLDA on HOG [19] 99.17% The proposed scheme using KNN 99.4% PCA on HOL [19] 99.73% KPCA on HOL [19] 99.73% The proposed scheme using SVM 100% classification problems.…”

Section: Methodsmentioning

confidence: 99%

Palmprint recognition using deep scattering network

Minaee

Wang

2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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Palmprint recognition has drawn a lot of attention during the recent years. Many algorithms have been proposed for palmprint recognition in the past, majority of them being based on features extracted from the transform domain. Many of these transform domain features are not translation or rotation invariant, and therefore a great deal of preprocessing is needed to align the images. In this paper, a powerful image representation, called scattering network/transform, is used for palmprint recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering network captures similar features to SIFT descriptors and the higher-layer features capture higher-frequency content of the signal which are lost in SIFT and other similar descriptors. After extraction of the scattering features, their dimensionality is reduced by applying principal component analysis (PCA) which reduces the computational complexity of the recognition task. Two different classifiers are used for recognition: multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rate of 99.95% and 100% using minimum distance classifier and SVM respectively.

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Section: Methodsmentioning

confidence: 99%

Palmprint recognition using deep scattering network

Minaee

Wang

2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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“…The parameters in these methods are the same as in [36] for the CASIA, IITD and PolyU 2D databases and for NTU-PI-v1 and NTU-CP-v1 are the same as those used for PolyU 2D contactless database. In the DOC [35], CR-CompCode [37], LLDP [33] and HOL [34], the codes provided by their authors and the default parameters are used. LLDP and HOL are used with Gabor filters.…”

Section: B Implementation Detailsmentioning

confidence: 99%

“…The evaluation protocols are described in Section V-A. In the experiments on NTU-PI-v1 and NTU-CP-v1, the ROIs extracted by the pretrained ROI-LAnet (Section V-D) are input to CompCode [29], OrdinalCode [30], DOC [35], DoN [36], DSCN [39], SIFT-IRANSAC [25], MF-LBP [26], VGG-16 [57], ResNet-50 [66], GoogLeNet [67], PalmNet [22], PCANet [42], AlexNet-S [21], CR-CompCode [37], LLDP [33] and HOL [34] because their preprocessing steps cannot handle images in these two databases, which have complex backgrounds or no gaps between the fingers. In other words, if the proposed ROI-LAnet is not used to "help" other palmprint recognition methods to extract ROI, all of them would fail.…”

Section: G Comparison With the State-of-the-art Palmprint Recognitiomentioning

confidence: 99%

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Matkowski

Chai

Kong

2020

IEEE Trans.Inform.Forensic Secur.

104

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Abstract-Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses highresolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is endto-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.

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“…In the more recent works, in [12], Jia proposed a new descriptor for palmprint recognition called histogram of oriented lines (HOL) which is inspired by the histogram of oriented gradients descriptors. The proposed descriptor has some robustness against small deformation and changes of illumination.…”

Section: Introductionmentioning

confidence: 99%

Highly accurate palmprint recognition using statistical and wavelet features

Minaee

Abdolrashidi

2015

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

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Palmprint is one of the most useful physiological biometrics that can be used as a powerful means in personal recognition systems. The major features of the palmprints are palm lines, wrinkles and ridges, and many approaches use them in different ways towards solving the palmprint recognition problem. Here we have proposed to use a set of statistical and wavelet-based features; statistical to capture the general characteristics of palmprints; and wavelet-based to find those information not evident in the spatial domain. Also we use two different classification approaches, minimum distance classifier scheme and weighted majority voting algorithm, to perform palmprint matching. The proposed method is tested on a well-known palmprint dataset of 6000 samples and has shown an impressive accuracy rate of 99.65%-100% for most scenarios.

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Histogram of Oriented Lines for Palmprint Recognition

Cited by 159 publications

References 34 publications

Palmprint recognition using deep scattering network

Palmprint recognition using deep scattering network

Palmprint Recognition in Uncontrolled and Uncooperative Environment

Highly accurate palmprint recognition using statistical and wavelet features

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