Fusion of dorsal palm vein and palm print modalities for higher security applications

Deshpande, Pallavi; Tavildar, Anil S.; Dandwate, Yogesh H.; Shah, Esha

doi:10.1109/casp.2016.7746165

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…The researchers used an autoregressive model to estimate the wavelet coefficient, and the K-nearest neighbor and support vector machine classifiers were used to classify the 600 palm photos. Deshpande et al (2016), according to the researchers, their system's accuracy, resilience, and anti-spoofing capabilities could be improved by integrating the palm vein and palm print techniques. They presented a system that uses a combination of these two biometric methods.…”

Section: Image Processingmentioning

confidence: 99%

Soft computing approach for feature extraction of palm biometric

Shende,

Shinde,

Wadhwa

et al. 2023

Multidiscip. Sci. J.

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The method for developing a secure and reliable identification system relies on the use of biometrics. In this case, the palm vein is used as a security measure. The proposed system is powered by a convolutional neural network, which is a type of neural network that is commonly utilized for image recognition. The palm vein's visual features are extracted using a convolutional neural network. This method can improve the recognition rate and its performance parameters. The results of an experiment conducted with this method were better than those obtained with conventional techniques.

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Section: Image Processingmentioning

confidence: 99%

Soft computing approach for feature extraction of palm biometric

Shende,

Shinde,

Wadhwa

et al. 2023

Multidiscip. Sci. J.

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show abstract

“…The maximum curvature method is used for extracting the vein pattern. Deshpande et al [81] also fused palmprint and palm vein. In the paper, palmprint features were extracted using a wavelet decomposition technique.…”

Section: Palm Vein Fusion Recognitionmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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“…4 Image data acquisition, feature extractions, and feature mapping techniques 4.1 Image capture process The data acquisition system (DAS) has been designed, developed, and fabricated. Regarding this DAS and the required image pre-processing, we have explained in detail in our paper published during the Conference on Advances in Signal Processing (CASP), 2016 [11]. DAS to extract PP modality is shown in Fig.…”

Section: System Effectivenessmentioning

confidence: 99%

“…The methodology for extraction of DWT features has been explained in detail in Pallavi Deshpande et al [11]. As indicated therein, two vectors of size 25 × 54, one for the approximate coefficients and the other for horizontal coefficients for each of the image, are stored in the database.…”

Section: Palm Print Feature Extraction Using Dwtmentioning

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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Fusion of dorsal palm vein and palm print modalities for higher security applications

Cited by 6 publications

References 20 publications

Soft computing approach for feature extraction of palm biometric

Soft computing approach for feature extraction of palm biometric

Review of palm vein recognition

Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

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