Online palmprint identification

Zhang, David; Kong, Wai-Kin; You, Jane; Wong, Michael

doi:10.1109/tpami.2003.1227981

Cited by 1,255 publications

(93 citation statements)

References 16 publications

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“…To solve the problem that there is not a standard criterion for evaluating the pattern similarity of images from two different domains, an quantized image pattern similarity index is formulated as calculating the number of correlated dictionary atoms from the perspective of reconstructing image based on sparse dictionary. Based on the proposed theory, simple pattern similarity evaluation experiments with different face images [5], [6], PolyU palmprint [7] and fingerprint images [8] and also the lab-made hand-dorsa vein images are conducted and results as expected are obtained.…”

Section: Introductionmentioning

confidence: 79%

Image Pattern Similarity Index and Its Application to Task-Specific Transfer Learning

Wang

2017

IEICE Trans. Inf. & Syst.

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Section: Introductionmentioning

confidence: 79%

Image Pattern Similarity Index and Its Application to Task-Specific Transfer Learning

Wang

2017

IEICE Trans. Inf. & Syst.

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“…Palmprint and face and palmprint and fingerprint pseudo multi-modal datasets were similarly formed, by pairing Poly-U palmprints (Zhang, Kong, You, & Wong, 2003) with the above face and fingerprint datasets, respectively. The interactions of the feature processing modules were investigated by conducting preliminary experiments on the organised pseudo multi-modal datasets, summarised in Table 1. The SDUMLA fingerprint dataset consists of fingerprint images that vary in quality, from partials with absent core points to fingerprints with average quality ridges.…”

Section: Preliminary Experiments For Developing the Guidelinesmentioning

confidence: 99%

“…Thank you to the authors of the publicly available datasets used in this paper: Poly-U Palmprint (Zhang et al, 2003), ORL Face (Samaria & Harter, 1994), SDUMLA-HMT (Yin et al, 2011), IITD Palmprint (Kumar, 2008), MCYT Fingerprint (Ortega-Garcia et al, 2003), MUCT Face (Milborrow et al, 2010) and CASIA Palmprint (Sun et al, 2005).…”

Section: Acknowledgementsmentioning

confidence: 99%

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Brown

Bradshaw

2017

SACJ

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The feature level, unlike the match score level, lacks multi-modal fusion guidelines. This work demonstrates a new approach for improved image-based biometric feature-fusion. The approach extracts and combines the face, fingerprint and palmprint at the feature level for improved human identification accuracy. Feature-fusion guidelines, proposed in our recent work, are extended by adding a new face segmentation method and the support vector machine classifier. The new face segmentation method improves the face identification equal error rate (EER) by 10%. The support vector machine classifier combined with the new feature selection approach, proposed in our recent work, outperforms other classifiers when using a single training sample. Feature-fusion guidelines take the form of strengths and weaknesses as observed in the applied feature processing modules during preliminary experiments. The guidelines are used to implement an effective biometric fusion system at the feature level, using a novel feature-fusion methodology, reducing the EER of two groups of three datasets namely: SDUMLA face, SDUMLA fingerprint and IITD palmprint; MUCT Face, MCYT Fingerprint and CASIA Palmprint.

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“…Various ways and techniques have been presented to surmount security problems. One such technique is the use of biometrics (Zhang et al, 2003;Duta, 2009). Biometrics refers to the use of physiological or biological features to recognize the identity of an individual.…”

Section: Introductionmentioning

confidence: 99%

Palmprint Verification Using Invariant Moments Based on Wavelet Transform

Hussein

Nordin

2014

Journal of Computer Science

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Data security is one of the important issues among computer users. Data security can prevent fraudulent users from accessing an individual's personal data. The biometrics recognition as one of the most important parts in the security of the data and the application of computer vision. The biometrics is the authentication method used in a wide variety of applications such as e-banking, e-commerce, e-government and many others. A biometric system is one which requires the recognition of a pattern, whereby it enables the differentiation of features from one individual to another. Biometric technologies, thus may be defined as the automated methods of identifying, or authenticating, the identity of a living person based on physiological or behavioral traits. This study emphasizes palmprint recognition, which provides a wide deployment range of authentication methods. The palmprint contains principal lines, wrinkles, fine lines, ridges and surface area; thus the palmprint of person differs from one to another. Previous researchers have difficulty extracting the features of a palm print, because of the effects of rotation, translation and scaling changes and the accuracy rate of verification performance needs to be improved. The aim of this study is to extract shape features using an invariant moments algorithm based on wavelet transform and identify the person's verification. This model has shown a promising results without the effects of rotation, translation and scaling of objects, because it is associated with the use of a good description of shape features. This system has been tested using databases from the Indian Institute of Technology, Kanpur (IITK), by using the False Rejection Rate (FRR) and False Acceptance Rate (FAR), we may calculate the accuracy rate of verification. The experiment shows a 97.99% accuracy rate of verification.

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Online palmprint identification

Cited by 1,255 publications

References 16 publications

Image Pattern Similarity Index and Its Application to Task-Specific Transfer Learning

Image Pattern Similarity Index and Its Application to Task-Specific Transfer Learning

Feature-Fusion Guidelines for Image-Based Multi-Modal Biometric Fusion

Palmprint Verification Using Invariant Moments Based on Wavelet Transform

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