A wavelet-based dominant feature extraction algorithm for palm-print recognition

Imtiaz, Hafiz; Fattah, Shaikh Anowarul

doi:10.1016/j.dsp.2012.06.016

Cited by 34 publications

(11 citation statements)

References 16 publications

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“…DWT technique uses joint time-frequency domain approach, hence pixel by pixel comparison is not required. Further, the degradations due to rotation, size, and brightness effects are much less severe for DWT [13,27]. Typical ROC curves for PP modality using DWT technique are plotted for acquired error free database of 150 users and are shown in Fig.…”

Section: Hcd Technique-based Biometric Recognition Systemmentioning

confidence: 99%

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Deshpande

Mukherji²,

Tavildar³

2019

EURASIP J. on Info. Security

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A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GAR min and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GAR min , the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.

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Section: Hcd Technique-based Biometric Recognition Systemmentioning

confidence: 99%

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Deshpande

Mukherji²,

Tavildar³

2019

EURASIP J. on Info. Security

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“…Wavelet transform, 28,29 contourlet and nonsubsampled contourlet transforms, 22 and dual tree complex wavelet transform (DTCWT) 31 have also been utilized to extract features. ROI is decomposed into subbands using these transforms and their coefficients are coded into bits.…”

Section: Related Workmentioning

confidence: 99%

“…The directional energy of the binary coded bits or other statistical features of subblocks of coefficients have been used as palmprint features. 28,29 Also, several discrimination analysis techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), Kernel PCA (KPCA), locality preserving projections (LPP), and 2-DLPP have been used to extract low-dimensional palmprint features. 21,25,[39][40][41] Uniform LBP histograms of subblocks of DTCWT coefficients obtained at two-level decomposition have been used as shift and grayscale invariant features for palmprint identification.…”

Section: Related Workmentioning

confidence: 99%

Blur and occlusion invariant palmprint recognition with block-wise local phase quantization histogram

Tamrakar

Khanna

2015

J. Electron. Imaging

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A method for palmprint verification and identification, which is robust to blurred and occluded palmprints is proposed. An algorithm is proposed for region of interest (ROI) extraction that aligns each palmprint ROI on the same position. First-level decomposition of the ROI using a Haar wavelet gives an approximation ROI (AROI) and reduces computational overhead. Local phase quantization (LPQ) based on quantization of phase information by local Fourier transform is applied on AROI to obtain blur invariant palmprint features. The LPQ image is divided into M × M nonoverlapping blocks, and histograms of these blocks are considered as features. The entropy of each block is used to identify it as occluded or nonoccluded. The average chi-square distance between corresponding nonoccluded blocks is calculated for measuring similarity. Experiments performed on PolyU 2D, CASIA, IITD, and IIITDMJ palmprint databases show that the proposed system is independent of acquisition device. Moreover, to justify the robustness of the proposed system, experiments are performed with variable amounts of blur and occlusion present in test palmprints. The performance of the proposed system is compared with several local feature-based palmprint recognition techniques on normal as well as occluded palmprints.

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“…Palm print Recognition system use prominent palm-line features, texture, global texture energy, or combination of these characteristics discussed in [13]. A frequency domain feature extraction algorithm for palm-print recognition is proposed [14], which efficiently exploits the local spatial variations in a palm print image based on extracting dominant spectral features from each of these bands using two dimensional discrete cosine transform (2D-DCT). A scannerbased personal authentication system by using the palm print features is proposed in [15].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Biometrics for Person Authentication using Hand Image

Sumathi¹,

Malini²

2013

IJCA

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To obtain secure and accurate authentication system using most accepted biometrics Hand geometry in multimodal approach. Implement multi modal person authentication system using single input image with efficient features. In this proposed method Hand geometry and selected window size palm print features computed from same image is used for authentication. Integrating the features increases robustness of the system. Discrete Wavelet Transform (DWT) is used for feature extraction and Support Vector Machine (SVM) is proposed for classification. The final decision is made by fusion at matching score level. Experiments are carried out on the publicly available GPDS Hand Database. The achieved experimental results GAR=99.47%, FAR=0% showed the effectiveness of system considered for high security applications.

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A wavelet-based dominant feature extraction algorithm for palm-print recognition

Cited by 34 publications

References 16 publications

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Blur and occlusion invariant palmprint recognition with block-wise local phase quantization histogram

Multimodal Biometrics for Person Authentication using Hand Image

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