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

Badrinath, G. S.; Tiwari, Kamlesh; Gupta, Phalguni

doi:10.1007/978-3-642-31588-6_76

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…Palm images of PolyU database are segmented using [5] to extract a square region of interest (ROI) as shown in Fig. 2(f).…”

Section: Resultsmentioning

confidence: 99%

“…Error rates are utilized in [7]. Biometrics systems such as palmprint [22], [23], [5], fingerprint [25], [28], [20], knuckle [4], vein pattern [21] and their applications [24], [30] are more apt to adaptive fusion. There is a need for a sophisticated fusion scheme which is adaptive to the underline statistical properties of a biometric system such as error rate and accuracy for their normalization and fusion.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TARC: A novel score fusion scheme for multimodal biometric systems

Tiwari

Nigam

Gupta

2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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This paper proposes a score level fusion scheme for a multimodal biometric system. Accuracy and reliability of a system are improved by utilizing more than one samples. Every matching of a biometric sample with its corresponding biometric sample in the database produces a matching score. There multiple scores from different biometric samples are fused for further utilization. It proposes an efficient threshold alignment and range compression scheme for score normalization. It uses statistical properties of biometric score distribution. The proposed scheme has been tested over a multimodal database which is constructed by using three publicly available database viz. FVC2006-DB2-A of fingerprint, CASIA-V4-Lamp of iris and PolyU of palmprint. Experimental results have shown the significant performance boost.978-1-4799-4533-7/14/$31.00 ©2014 IEEE

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“…Palm images of PolyU database are segmented using [5] to extract a square region of interest (ROI) as shown in Fig. 2(f).…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TARC: A novel score fusion scheme for multimodal biometric systems

Tiwari

Nigam

Gupta

2014

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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“…Moreover, palmprint also serves as a reliable human identifier because the print patterns are not found to be duplicated even in mono-zygotic twins. [1,2] So, in this work, an attempt is made to authenticate a person using palmprint.…”

Section: Biometric Technologiesmentioning

confidence: 99%

Transform based approaches for Palmprint Identification

Vaidehi¹,

Subashini²,

Ramalingam³

et al. 2012

IJCA

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In this paper, identification of a person is carried out using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) coefficients as palmprint features. Dimensionality reduction was carried out using Principal Component Analysis (PCA) and the reduced features were classified using Support Vector Machine (SVM). The segmented palmprint images used in this study are from the IITD palmprint database. Here, we describe the palmprint identification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Experiments were developed on a database of 100 images from 20 individuals. This study shows that DCT is better for palmprint identification with an accuracy of 99% compared to DWT which gives 96.66% accuracy.

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“…Behavioral as well as physiological biometrics based characteristics (such as face [1,2,3], fingerprint [4], iris [5,6], palmprint [7,8], knuckleprint [9,10], gait, voice,vein patterns etc.) are used to develop robust, accurate and highly efficient personal authentication systems.…”

Section: Introductionmentioning

confidence: 99%

Quality assessment of knuckleprint biometric images

Nigam

Gupta

2013

2013 IEEE International Conference on Image Processing

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Image quality can play key role in the system performance. The recently introduced knuckleprint biometric has shown promising results, but its quality assessment is difficult because it lacks well defined and structured features as in the case of face or fingerprint. To our knowledge this is the first attempt to automatically assess the quality of knuckleprint images. The quality of knuckleprint images mainly depends upon the vertical line like features, focus, contrast and reflections produced by the camera flash. In this paper an effort has been made to identify, estimate and quantify some of these quality attributes and fuse them to obtain an overall quality score for any knuckleprint image. The largest publicly available PolyU knuckleprint database is used for testing, containing 7920 images. Extensive study is being carried out in order to establish a relationship between image quality and matching performance in order to demonstrate the proposed framework's utility.

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

Cited by 20 publications

References 12 publications

TARC: A novel score fusion scheme for multimodal biometric systems

TARC: A novel score fusion scheme for multimodal biometric systems

Transform based approaches for Palmprint Identification

Quality assessment of knuckleprint biometric images

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