Combining Left and Right Palmprint Images for More Accurate Personal Identification

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Engineering

COMBINING LEFT AND RIGHT PALMPRINT IMAGES FOR
MORE ACCURATE PERSONAL IDENTIFICATION
ABSTRACT
Multibiometrics can provide higher identification accuracy than single biometrics, so it is
more suitable for some real-world personal identification applications that need high-standard
security. Among various biometrics technologies, palmprint identification has received much
attention because of its good performance. Combining the left and right palmprint images to
perform multibiometrics is easy to implement and can obtain better results. However, previous
studies did not explore this issue in depth. In this paper, we proposed a novel framework to
perform multibiometrics by comprehensively combining the left and right palmprint images.
This framework integrated three kinds of scores generated from the left and right palmprint
images to perform matching score-level fusion. The first two kinds of scores were, respectively,
generated from the left and right palmprint images and can be obtained by any palmprint
identification method, whereas the third kind of score was obtained using a specialized algorithm
proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right
palmprint images into account, it can properly exploit the similarity of the left and right
palmprints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect
identification performance to be obtained in comparison with previous palmprint identification
methods.

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This paper proposes a Bio metric identification system for person identification using two bio metric traits hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify key points on palm region of hand. Identified key points are used to find hand geometry feature and palm print Region of interest (ROI). The discriminative palm print features are extracted by applying local binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the input image (probe) is determined by comparing probe template with the templates of all identities enrolled in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.

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This paper introduces a device, algorithm and graphical user interface to obtain anthropometric measurements of foot. Presented device facilitates obtaining scale of image and image processing by taking one image from side foot and underfoot simultaneously. Introduced image processing algorithm minimizes a noise criterion, which is suitable for object detection in single object images and outperforms famous image thresholding methods when lighting condition is poor. Performance of image-based method is compared to manual method. Image-based measurements of underfoot in average was 4mm less than actual measures. Mean absolute error of underfoot length was 1.6mm, however length obtained from side foot had 4.4mm mean absolute error. Furthermore, based on t-test and f-test results, no significant difference between manual and image-based anthropometry observed. In order to maintain anthropometry process performance in different situations user interface designed for handling changes in light conditions and altering speed of the algorithm.

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Identification of person using multiple biometric is very common approach used in existing user validation of systems. Most of multibiometric system depends on fusion schemes, as much of the fusion techniques have shown promising results in literature, due to the fact of combining multiple biometric modalities with suitable fusion schemes. However, similar type of practices are found in ensemble of classifiers, which increases the classification accuracy while combining different types of classifiers. In this paper, we have evaluated comparative study of traditional fusion methods like feature level and score level fusion with the well-known ensemble methods such as bagging and boosting. Precisely, for our frame work experimentations, we have fused face and palmprint modalities and we have employed probability model - Naive Bayes (NB), neural network model - Multi Layer Perceptron (MLP), supervised machine learning algorithm - Support Vector Machine (SVM) classifiers for our experimentation. Nevertheless, machine learning ensemble approaches namely, Boosting and Bagging are statistically well recognized. From experimental results, in biometric fusion the traditional method, score level fusion is highly recommended strategy than ensemble learning techniques.

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Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden.

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This paper presents an optimized palm vein authentication algorithm which will match the similar type of vascular pattern from the database given i.e. CASIA-Palmprint V1 dataset. . The palm vein authentication technology offers a high level of accuracy. Palm vein authentication uses the vascular patterns of an individualÃ¢â‚¬â„¢s palm as personal identification data. If we compare with a finger or the back of a hand, a palm has a broader and more complicated vascular pattern and thus contains a wealth of differentiating features for personal identification. The importance of biometrics in the current field of Security has been depicted in this work. Also, we have also outlined opinions about the utility of Biometric authentication systems. We have processed the raw image from the dataset before implementing authentication algorithm. After getting the suitable image after preprocessing, we have used local binary pattern (LBP) for feature extraction purpose & then using a machine learning algorithm, with support vector machine (SVM), we tried to match the vascular vein pattern for authentication. Result of the matching algorithm is not only optimized as per the proposed approach but also quite efficient.

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Biometrics recognition system is more reliable and popular. In this paper we describe a palmprint and handgeometry based person identification consisting of three main steps - preprocessing techniques such as morphological operations. The feature extraction techniques such as FAST feature algorithm and region properties is used to independently extract palmprint and handgeometry features. Feature matching with euclidean distance classifier. These techniques are more reliable and faster than traditional techniques used. We finally conclude that the proposed methodology has better performance .This is supported by our experiments which are able to achieve recognition rate for palmprint 100 % and for handgeometry 93.75 %.

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International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.

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Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.

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Combining Left and Right Palmprint Images for More Accurate Personal Identification

1. COMBINING LEFT AND RIGHT PALMPRINT IMAGES FOR MORE ACCURATE PERSONAL IDENTIFICATION ABSTRACT Multibiometrics can provide higher identification accuracy than single biometrics, so it is more suitable for some real-world personal identification applications that need high-standard security. Among various biometrics technologies, palmprint identification has received much attention because of its good performance. Combining the left and right palmprint images to perform multibiometrics is easy to implement and can obtain better results. However, previous studies did not explore this issue in depth. In this paper, we proposed a novel framework to perform multibiometrics by comprehensively combining the left and right palmprint images. This framework integrated three kinds of scores generated from the left and right palmprint images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palmprint images and can be obtained by any palmprint identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palmprint images into account, it can properly exploit the similarity of the left and right palmprints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palmprint identification methods.

Combining Left and Right Palmprint Images for More Accurate Personal Identification

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