Palmprint Identification Integrating Left and Right by Local Discriminant Canonical Correlation Analysis (original) (raw)
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Personal Authentication Using the Combination of Left and Right Palmprint Images
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This paper develops the accurate personal identification by combining the left and right palmprint images. The multibiometrics has received much attention in the palmprint identification. We, firstly propose a new approach for image level combination of RGB color to generate more reliable palmprint representation than the conventional gray level representation. This investigation is motivated to develop the similarity between the left and right palmprint images. Secondly this paper analysis the feature representation (SIFT and OLOF) and different database (Poly U and IITD). Then the left and right images to perform matching score-level fusion. The experimental results from this study suggests that the Scale Invariant Feature Transform (SIFT) and Orthogonal Line Ordinal Features, perform significantly better for the contactless palmprint images than other approach employed earlier on the more conventional palmprint imaging. The achieved error rates show a good performance of these fe...
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In the networked society there are a great number of systems that need biometric identification, so it has become an important issue in our days. This identification can be based on palmprint features. In this work we present a biometric identification system based on palmprint local features. For this purpose, we have compared the error rate (ER) of 3 different preprocessing methods.
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In this paper, an effective biometrics method based on hand geometry is presented for biometric identification or verification system. Biometrics-based authentication is a verification approach using the biological features inherent in each individual. They are processed based on the identical, portable, and arduous duplicate characteristics. The principal lines and texture are two kinds of salient features of palmprint. A few years ago , a new branch of biometrics technology, palmprint authentication was proposed whereby line and points are extracted from palms for personal identification. In this paper, we consider palmprint as a piece of lines and texture and apply palmprint identification techniques for extracting feature to palmprint authentication.
A Novel Approach for Automatic Palmprint Recognition
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In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).
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International Journal of Computer Applications, 2012
This paper presents some new features for the palmprint based authentication. The Region of interest (ROI) is extracted from the palmprint image by finding a tangent to the curves between fingers. The perpendicular bisector of this tangent and the tangent itself help demarcate the rectangular area that forms the ROI of the palmprint. Four approaches are presented for the feature extraction. In the first approach the ROI is divided into a suitable number of non-overlapping windows from which fuzzy features are extracted. In the second approach multi-scale wavelet decomposition is applied on the ROI and the detail images are combined to yield a composite image which is partitioned into non-overlapping windows and energy features are extracted. In the third approach sigmoid features are extracted from the ROI and in the fourth approach feature extraction is done using Local Binary Pattern (LBP) based on the
Personal authentication using multiple palmprint representation
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Although several palmprint representations have been proposed for personal authentication, there is little agreement on which palmprint representation can provide best representation for reliable authentication. In this paper, we characterize user's identity through the simultaneous use of three major palmprint representations and achieve better performance than either one individually. This paper also investigates comparative performance between Gabor, line and appearance based palmprint representations and using their score and decision level fusion. The combination of various representations may not always lead to higher performance as the features from the same image may be correlated. Therefore we also propose product of sum rule which achieves better performance than any other fixed combination rules. Our experimental results on the database of 100 users achieve 34.56% improvement in performance (equal error rate) as compared to the case when features from single palmprint representation are employed. The proposed usage of multiple palmprint representations, especially on the peg-free and non-contact imaging setup, achieves promising results and demonstrates its usefulness.
Discriminant Orientation Feature for Palmprint Recognition
2013 13th International Conference on Computational Science and Its Applications, 2013
In this paper, we propose a novel feature for palmprint recognition, called Discriminant Orientation Feature (DORIF) based on using Modified Finite Radon Transform (MFRAT) and Two Directional Two Dimensional Linear Discriminant Analysis (2D) 2 LDA. Our proposed method includes two main steps for palmprint feature extraction: (1) Local invariant orientation features are extracted by using MFRAT that handles the palm structure and the variations of illumination and rotation. (2) (2D) 2 LDA is then applied to further remove redundant information and form a discriminant representation more suitable for palmprint recognition. The experimental results for the identification on public database of Hong Kong Polytechnic University (PolyU) demonstrate the effectiveness of the proposed method.
A Review on Palmprint Recognition
2017
Biometrics is defined as the exceptional or individual physical properties or attributes of human body. These attributes are used to recognize each human. Any details of the person body which differs from another person to the other is used as unique biometrics data which serve to that’s person’s unique ID. Palmprint recognition being one of the important aspects of biometric technology. These palmprint recognition serves into four stages, palmprint image acquisition, preprocessing, feature extraction and matching. The major approach for palmprint recognition is to extract feature vector from each individual palm and to perform matching based on some distance metrics. This paper present a detailed review on palmprint recognition approaches.
Palm prints have many biometric features and these features can be used for the purpose of biometric identification. This paper presents use of Principal Component Analysis for feature extraction of palm print images for biometry. A prefixed number of Eigen values have been retained and corresponding Eigen vectors have been used to generate a weight matrix for every palm print image. The weight matrix has been used as a feature vector. Feature matching has been done with Neural Network, Weighted Euclidean distance as well as Pattern classifiers Networks and the performance has been analyzed in this paper.
Hand Geometry and Palmprint Classification System Based on Statistical Analysis
Journal of Al-Nahrain University of Science
Biometric system is considered of an important type of security systems nowadays, because it relays on the individual traits (physical or behavioral) non-participation between any two people that can't be lost on lifetime and can't be stolen. This paper, will present the individual classification system based on hand geometry and palm texture feature where it is one of the parts of the human body, which has an impressive set of information capable to distinguish and identify individuals. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system consists of three phases: image preprocessing, hand feature extraction and pattern classification. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system utilized complete hand image inside database consists of 600 pictures, include 100 people, each one has six images. Experimental results show that 98.3% is achieved and that illustrate the applicability of the system in the security's average of different environments.