Palmprint verification using hierarchical decomposition

Lin, Chih‐Lung; Chuang, Thomas C.; Fan, Kuo-Chin

doi:10.1016/j.patcog.2005.04.001

Cited by 91 publications

(35 citation statements)

References 26 publications

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“…We used the palmprint database developed at Biometric Research Center at Hong Kong Polytechnic University. In preprocessing stage, we used technique (Lin et al, 2005) to extract the ROI region.. Before extracting the ROI, adaptive median filter (Gonzalez and Woods, 2009) is used to smoothen the image. The advantage of adaptive filter is that it seeks to preserve details while smoothing non-impulse noise, something that the traditional filter does not do.…”

Section: Preprocessing and Segmentationmentioning

confidence: 99%

Intramodal palmprint recognition using texture feature

Latha¹,

Prasad²

2017

IJISDC

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Palmprint technology is a new branch of biometrics used to identify an individual. Palmprint has rich set of features like palm lines, wrinkles, minutiae points, texture, ridges, etc. Several line and texture extraction techniques for palmprint have been extensively studied. This paper presents an intramodal authentication system based on texture information extracted from the palmprint using the Haralick features, 2D-Gabor and 2D-log Gabor filters. An individual feature vector is computed for a palmprint using the extracted texture information of each filter type. Performance of the system using three feature types is evaluated individually. Finally, we combine the three feature types using feature level fusion to develop an intramodal palmprint recognition system. The experiments are evaluated on a standard benchmark database (PolyU Database), and the results shows that significant improvement in terms of recognition accuracy and error rates with the proposed intramodal recognition system compared to individual representations.

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Section: Preprocessing and Segmentationmentioning

confidence: 99%

Intramodal palmprint recognition using texture feature

Latha¹,

Prasad²

2017

IJISDC

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“…Principal palmprint features were extracted using directional and multi resolution decompositions. To test the proposed methodology 4800 palmprint images were collected from 160 individuals [13]. The proposed method was able to achieve 0.69% FAR when the FRR is at 0.75%.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification

Krishneswari¹,

Arumugam²

2011

IJCA

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Palmprint is emerging as a popular biometric based personal identification technique and has been found to be more advantageous than fingerprint because of its larger area to capture more distinctive features. Most of the fingerprint discriminative features are also found in Palmprints. Palmprint feature extraction is one of the most important stages in the verification process. The robustness of the system depends on the feature extraction methodology and its ability to extract features from the palmprint. In this paper we propose a global feature extraction based on the Discrete Cosine Transform and investigate the efficiency of BayesNet algorithm for verification. This work also investigated the effect of feature reduction using information gain on the proposed methodology. This work utilized 50 palmprints of different users from the palmprint database provided by the Hong Kong Polytechnic University (HK-PolyU) to evaluate the proposed methodology.

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“…Scanner-based hand biometric systems have been proposed in [1,2,3]. However, scanners are slow and require the hand to be placed on the scanning surface.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Model-Based Hand Localization for Unsupervised Palmar Image Acquisition

Fratrić

Ribarić

2009

Advances in Biometrics

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Abstract. Unsupervised and touchless image acquisition are two problems that have recently emerged in biometric systems based on hand features. We have developed a real-time model-based hand localization system for palmar image acquisition and ROI extraction. The system operates on video sequences and produces a set of palmprint regions of interest (ROIs) for each sequence. Hand candidates are first located using Viola-Jones approach and then the best candidate is selected using model-fitting approach. Experimental results demonstrate the feasibility of the system for unsupervised palmar image acquisition in terms of speed and localization accuracy.

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Palmprint verification using hierarchical decomposition

Cited by 91 publications

References 26 publications

Intramodal palmprint recognition using texture feature

Intramodal palmprint recognition using texture feature

Investigation of Probabilistic Graphical Model Algorithms for Palm print Verification

Real-Time Model-Based Hand Localization for Unsupervised Palmar Image Acquisition

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