Concavity-orientation coding for palmprint recognition

Tabejamaat, Mohsen; Mousavi, Ali

doi:10.1007/s11042-016-3544-6

Cited by 27 publications

(13 citation statements)

References 33 publications

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“…In above discussed earlier work, we have seen that, it was done on the different extraction methods of feature extraction from multispectral palmprint (Red, Green and Blue) [5][6][7][8].In this paper we are performing score level fusion of multispectral palmprint using triangular conorm. Earlier work on score level fusion has been done on triangular norm [2][3].…”

Section: Proposed Approachmentioning

confidence: 99%

“…This work was divided into two parts first part constitute the extraction of Block Dominant Orientation Code(BDOC) as a rough feature, and then the Block based Histogram of Oriented Gradient (BHOG) as a fine feature.In second part a hierarchical recognition approach was proposed based on two types of featutres extracted in first part. Then finally the fusion of features of different bands were performed to improve the accuracy.In [8] an investigation for the use of concavity feature in different orientation of palmprint recognition.…”

Section: Related Workmentioning

confidence: 99%

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Score Level Fusion of Multispectral Palmprint with Triangular Conorm

Afzal

Ahad

Grover

2018

IJERMT

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Section: Proposed Approachmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Score Level Fusion of Multispectral Palmprint with Triangular Conorm

Afzal

Ahad

Grover

2018

IJERMT

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“…In Kong and Zhang's study (Kong & Zhang, 2004), authors used CompCode by encoding the orientation information of lines with the help of multiple 2D Gabor filters. In addition to this, palm orientation code (Wu, Wang, & Zhang, 2005), robust line orientation code (Jia, Huang, & Zhang, 2008), discriminative competitive code (Leng & Zhang, 2011), double orientation code (Fei, Xu, Tang, & Zhang, 2016), competitive orientation map (Tabejamaat & Mousavi, 2017), and so forth are well defined in the literature. Later, cancellable coding schemes based on texture features have been suggested.…”

Section: Related Workmentioning

confidence: 99%

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

Jaswal

Poonia

2020

Expert Systems

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The impact of digital technology in biometrics is much more efficient at interpreting data than humans, which results in completely replacement of manual identification procedures in forensic science. Because the single modality-based biometric frameworks limit performance in terms of accuracy and anti-spoofing capabilities due to the presence of low quality data, therefore, information fusion of more than one biometric characteristic in pursuit of high recognition results can be beneficial. In this article, we present a multimodal biometric system based on information fusion of palm print and finger knuckle traits, which are least associated to any criminal investigation as evidence yet. The proposed multimodal biometric system might be useful to identify the suspects in case of physical beating or kidnapping and establish supportive scientific evidences, when no fingerprint or face information is present in photographs. The first step in our work is data preprocessing, in which region of interest of palm and finger knuckle images have been extracted. To minimize nonuniform illumination effects, we first normalize the detected circular palm or finger knuckle and then apply line ordinal pattern (LOP)-based encoding scheme for texture enrichment. The nondecimated quaternion wavelet provides denser feature representation at multiple scales and orientations when extracted over proposed LOP encoding and increases the discrimination power of line and ridge features. To best of our knowledge, this first attempt is a combination of backtracking search algorithm and 2D 2 LDA has been employed to select the dominant palm and knuckle features for classification. The classifiers output for two modalities are combined at unsupervised rank level fusion rule through Borda count method, which shows an increase in performance in terms of recognition and verification, that is, 100% (correct recognition rate), 0.26% (equal error rate), 3.52 (discriminative index), and 1,262 m (speed).

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“…Also, to acquire these visible features, it is necessary to take the images of the palms with several spectral bands. Recently, the multispectral palmprint biometrics have captured attention over the past 15 decades [6,19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

Thamri¹,

Aloui²,

Naceur³

2020

IJACSA

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Nowadays, there are several identification systems which are based on different biometric modalities. In particular, multispectral images of palmprints captured in different spectral bands have a very distinctive biometric identifier. This paper proposes a novel fusion scheme of a biometric recognition system by multiSpectral palmprint. This system is composed of three blocks: (1) extraction of the region of interest (ROI) from multispectral images, (2) a new image fusion architecture based on the measurement of decorrelation, and (3) a scheme of dimension reduction and classification. The proposed image fusion system combines the information from the same left and right spectral band using the 2D discrete wavelet (DWT) transform technique. In addition, a feature extraction using the Log-Gabor transform is performed, while the feature size has been reduced using the Kernel Principal Component Analysis technique (KPCA). In Our experiments we use CASIA multispectral palmprint database. We obtained an accuracy rate (ACC) of 99.50% for the spectral bands WHT (white light) and 940 nm and an equal error rate EER = 0.05%.These results show that our system is robust against spoofing.

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Concavity-orientation coding for palmprint recognition

Cited by 27 publications

References 33 publications

Score Level Fusion of Multispectral Palmprint with Triangular Conorm

Score Level Fusion of Multispectral Palmprint with Triangular Conorm

Selection of optimized features for fusion of palm print and finger knuckle‐based person authentication

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

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