Fusion of multiple features for palmprint authentication

Author name used in this publication: David ZhangVersion of RecordPublishe

Palmprint Verification Using Time Series Method

The use of biometrics as an automatic recognition system is growing rapidly in solving security problems; palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI) segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palm print. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036)

Palmprint Verification Using Time Series Method

The use of biometrics as an automatic recognition system is growing rapidly in solving security problems; palmprint is one of biometric system which often used. This paper used two steps in center of mass moment method for region of interest (ROI) segmentation and apply the time series method combined with block window method as feature representation. Normalized Euclidean Distance is used to measure the similarity degrees of two feature vectors of palm print. System testing is done using 500 samples palms, with 4 samples as the reference image and the 6 samples as test images. Experiment results show this system can achieve a high performance with success rate about 97.33% (FNMR=1.67%, FMR=1.00 %, T=0.036)

Palm print verification based deep learning

In this paper, we consider a palm print characteristic which has taken wide attentions in recent studies. We focused on palm print verification problem by designing a deep network called a palm convolutional neural network (PCNN). This network is adapted to deal with two-dimensional palm print images. It is carefully designed and implemented for palm print data. Palm prints from the Hong Kong Polytechnic University Contact-free (PolyUC) 3D/2D hand images dataset are applied and evaluated. The results have reached the accuracy of 97.67%, this performance is superior and it shows that our proposed method is efficient

Palmprint identification using restricted fusion

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A Survey Paper on Palm Prints Based Biometric Authentication System

In this paper we are providing an approach for authentication using palm prints. Reliability in computer aided personal authentication is becoming increasingly important in the information-based world, for effective security system. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant and easy to acquire. Palm print is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich information of palm print offers one of the powerful means in personal recognition

Multispectral Palmprint Recognition Using a Quaternion Matrix

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%