A feature level multimodal approach for palmprint identification using directional subband energies

Mansoor, Asif; Masood, Hassan; Mumtaz, Muhammad Zubair; Khan, Shoab Ahmed

doi:10.1016/j.jnca.2010.08.004

Cited by 22 publications

(8 citation statements)

References 29 publications

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“…No matter what image enhancement technique is applied it would not be able to compensate for motion blur and de-focused image without affecting the truthfulness of pixels. As mentioned in our previous research on a peg-free system [27], [28], [29] we have advocated that even slight image rotation changes the original image parameters and induces a certain blur which in turn degrades the performance of the system. In order to get a pure image we have endeavored to improve upon the process, the subjects and the capturing device before eventual image acquisition.…”

Section: Image Quality and Biometricsmentioning

confidence: 97%

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

Mumtaz¹,

Masood²,

Mumtaz³

2012

AJSP

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The ever increasing demand of security has resulted in wide use of Biometric systems. Despite overcoming the traditional verification problems, the unimodal systems suffer from various challenges like intra class variation, noise in the sensor data etc, affecting the system performance. These problems are effectively handled by multimodal systems. User psychology is accounted has been taken into consideration which highly effects its eventual user acceptability and collectability among masses. Desirable image quality is achieved by a stable and user friendly image acquisition platform. In this paper, we present multimodal approach for palm and fingerprints by feature level and score level fusions (sum and product rules). The proposed multimodal systems are tested on a developed database consisting of 440 palm and fingerprints each of 55 individuals. In feature level fusion, directional energy based feature vectors of palm-and fingerprint identifiers are combined to form joint feature vector that is subsequently used to identify the individual using a distance classifier. In score level fusion, the matching scores of individual classifiers are fused by sum and product rules. Receiver operating characteristics curves are formed for unimodal and multimodal systems. Equal Error Rate (EER) of 0.538% for feature level fusion shows best performance compared to score level fusion of 0.6141 and 0.5482% of sum and product rules, respectively. Multimodal systems, however, significantly outperform unimodal palm and fingerprints identifiers with EER of 2.822 and 2.553%, respectively.

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Section: Image Quality and Biometricsmentioning

confidence: 97%

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

Mumtaz¹,

Masood²,

Mumtaz³

2012

AJSP

Self Cite

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“…Palmprint texture features are calculated by many techniques, such as Fourier transform 6 , Gabor transform 3 , Wavelet transform 7 , Directional sub-band decomposition 8 , Radon transform 9 , Local Binary Pattern (LBP) 10,11 , and many more 1,2 . These techniques rely on some unique invariant property which can be utilized to find the robust features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It has been reported that ULBP(8, 1) and ULBP (8,2) contain almost 90% of all pattern for texture images 16 . Increased number of neighbors to calculate ULBP histograms increases the size of feature vector as well as the computation time.…”

Section: Testing On Normal Palmprintsmentioning

confidence: 99%

“…Increased number of neighbors to calculate ULBP histograms increases the size of feature vector as well as the computation time. Performance of the proposed approach is tested on normal palmprints for ULBP(8, 1) and ULBP (8,2) with different number of partitions to find suitable combination of partition size M × M and neighborhood radius R. Table 2 gives the performance in terms of EER, DI and CIR for all three palmprint databases. It is found that performance of the system improves with increasing number of sub-images but up to a specific partition, i.e., 7 × 7 partitions for PolyU 2D and 4 × 4 partitions for CASIA and IIITDMJ databases.…”

Section: Testing On Normal Palmprintsmentioning

confidence: 99%

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Occlusion Invariant Palmprint Recognition with ULBP Histograms

Tamrakar

Khanna

2015

Procedia Computer Science

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This work presents a palmprint recognition approach based on the local distribution of uniform patterns. Preprocessing segments palmprint region from foreground of image as well as aligns the palmprint along the line between tips of middle finger and mid of wrist. Palmprint texture has line and wrinkles like features which are a type of uniform patterns. Uniform LBP (ULBP) refers to the pattern of uniform appearance with limited discontinuities. ULBP histograms are used as features to handle occlusion upto 36%. Experiments are performed on 400 classes of the PolyU 2D, 600 classes of CASIA and 150 classes of IIITDMJ palmprint database. Correct identification rates of 99.6% for normal palmprints and 99% for randomly occluded palmprints are obtained on PolyU 2D database.

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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%

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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A feature level multimodal approach for palmprint identification using directional subband energies

Cited by 22 publications

References 29 publications

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

User Psychology and Behavioral Effect on Palmprint and Fingerprint Based Multimodal Biometric System

Occlusion Invariant Palmprint Recognition with ULBP Histograms

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

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