A novel approach to eigenpalm features using feature-partitioning framework (original) (raw)
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Palmprint recognition using eigenpalms features
Pattern Recognition Letters, 2003
In this paper, we propose a palmprint recognition method based on eigenspace technology. By means of the Karhunen-Loeve transform, the original palmprint images are transformed into a small set of feature space, called ''eigenpalms'', which are the eigenvectors of the training set and can represent the principle components of the palmprints quite well. Then, the eigenpalm features are extracted by projecting a new palmprint image into the subspace spanned by the ''eigenpalms'', and applied to palmprint recognition with a Euclidean distance classifier. Experimental results illustrate the effectiveness of our method in terms of the recognition rate.
A Novel Approach for Automatic Palmprint Recognition
2007
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%).
AN ENHANCED PRINCIPAL COMPONENT ANALYSIS APPROACH FOR EXTRACTING FEATURES IN PALMPRINT IMAGES
— In reducing the dimensions of feature space so as to achieve better performance, minimal features that yet represent the original image maximally need to be extracted. Principal Component Analysis (PCA) which is an appearance based projection method has been widely used in this regards. Literatures have shown that PCA performs well for dimensionality reduction but instead of reducing within class variations, it increases it therefore resulting in its low performance. In contrast, Independent Component Analysis (ICA) performs preferably well in this instead since it linearly transforms an original data into completely independent components thereby reducing within class variations. While both PCA and ICA have been used for the purpose of feature extraction, no single feature extraction algorithm is exclusively flawless in all ramifications. In lieu of this, this paper introduces an Enhanced Principal Component Analysis (EPCA) approach which first extracts principal components from data and further uses the extracted components as input to an ICA algorithm. The EPCA was employed for feature extraction in a palmprint recognition system. The resultant system was validated using 900 palmprint images which were downloaded from three public palmprint databases. Recognition and verification rates arising from the palmprint recognition system showed that the EPCA performed well than PCA algorithm.
Subject Review: Feature Extraction Based on Palm Print
International Journal of Engineering Research and Advanced Technology (IJERAT), 2021
Physiological biometrics is one of the attractive fields for researchers due to its unique and stable features. One of these physical biometrics is "Palm Print". This new approach is used in personal recognition because of the powerful information that can be extracted from the palm print. The characteristics that are extracted keeping the rules of palmprint feature extraction are very important. In spite of the huge work done in this approach, the results recorded about the palm print are still uncompleted and the techniques that used in palm print feature extraction used in recognition are still continually modified. In this paper, we present a detailed background review for many techniques and methods that are used to extract features from palm print with many various methods and procedures. A comparison between these techniques is also presented.
Palmprint Recognition Using Hessian Matrix and Two-Component Partition Method
International Journal of Digital Crime and Forensics
Palmprint recognition has been comprehensively examined in the past couple of years and various undertakings are done to use it as a biometric methodology for various applications. The point of this study is to construct an effective palmprint recognition technique with low computational multifaceted nature and along these lines to expand the acknowledgment and precision. Since edges are free from distortion, they are very reliable and subsequently used for palm print recognition. The originality of the proposed technique depends on new area of interest (ROI) extraction took after by new principal line extraction and texture matching strategy. The new principal line extraction technique is created by using the Hessian matrix and Eigen value. The texture matching of the ROI is done using new 2-component partition method by segmenting the image into comparative and non-comparative edges. Examinations are finished on a database and exploratory results exhibit that the accuracy of the p...
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.
An automated palmprint recognition system
Image and Vision Computing, 2005
Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multifrequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database. q
Feature Extraction Techniques for Palmprint Identification: A Survey
Abstract— Palmprint recognition has been investigated over the past decade. Palmprint recognition has five stages palmprint acquisition, pre-processing, feature extraction, enrolment (database) and matching. Due to rich information in palmprint it became a powerful means in person identification. The major approach for palmprint recognition is to extract feature vectors corresponding to individual palm image and to perform matching based on some distance metrics. Palmprint recognition is a challenging problem mainly due to low quality of pattern, large nonlinear distortion between different impression of same palm and large image size, which makes feature extraction and matching computationally demanding. In this paper we talk about the various approaches of palmprint recognition using matching pattern method.
A new method in locating and segmenting palmprint into region-of-interest
… Recognition, 2004
Various techniques in analyzing palmprint have been proposed but to the best of our knowledge, none has been studied on the selection and division of the region-ofinterest (ROI). Previous methods were always applied only to a fixed size square region chosen as the central part of the palm, which were then divided into square blocks for extraction of local features. In this paper, we proposed a new method in locating and segmenting the ROI for palmprint analysis, where the selected region varies with the size of the palm. Instead of square blocks, the region is divided into sectors of elliptical half-rings, which are less affected by misalignment due to rotational error. More importantly, our arrangement of the feature vectors ensures that only features extracted from the same spatial region of two aligned palms will be compared with each other. Encouraging results obtained favor the use of this method in the future development of palmprint analysis techniques.
A comparison of feature normalization techniques for PCA-based palmprint recognition
Computing user templates (or models) for biometric authentication systems is one of the most crucial steps towards efficient and accurate biometric recognition. The constructed templates should encode user specific information extracted from a sample of a given biometric modality, such as, for example, palmprints, and exhibit a sufficient level of dissimilarity with other templates stored in the systems database. Clearly, the characteristics of the user templates depend on the approach employed for the extraction of biometric features, as well as on the procedure used to normalize the extracted feature vectors. While feature extraction methods are a well studied topic, for which a vast amount of comparative studies can be found in the literature, normalization techniques lack such studies and are only briefly mentioned in most cases. In this paper we, therefore, apply several normalization techniques to feature vectors extracted from palmprint images by means of principal component analysis (PCA) and perform a comparative analysis on the results. We show that the choice of an appropriate normalization technique greatly influences the performance of the palmprint-based authentication system and can result in error rate reductions of more than 30%.