Palmprint recognition using eigenpalms features (original) (raw)
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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%).
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.
Palmprint Recognition Using Transform Domain and Spatial Domain Techniques
2015
Palmprint is physiological biometric used for recognition of person. Palmprint containing texture, statistical, line, point, geometry features. In this paper we proposed palmprint recognition using DWT, DCT and PCA techniques (PRUDDP). It is simple and effective methodology for palmprint recognition. The preprocessing used for palmprint image alignment, resize the palmprint image and to enhance contrast of palmprint image by using histogram equalization. DCT and DWT used to generate features and that features are extracted by using PCA. The extracted features from database images and test images are match by using Euclidean Distance. Keywords— Biometric, DCT, DWT, ED, PCA, and Palmprint
An Advance Subspace Method for Implementing Palm Print Recognition
— Biometrics are very useful to identify individuals based on their behavioural and physiological features, that can be used for their personal authorization. Various physical features like, iris patterns, retina patterns, palm print patterns, fingerprint patterns, facial features etc. are used for such purposes. Palm print identification involves recognizing an specific by matching the various wrinkles, principal lines and creases on the surface of the palm of the hand. The base for using the palm prints lies in the fact that since palm print patterns are generated by random orientations of tissues and muscles of the hand during birth, no two individuals have exactly the same palm print pattern. Research of Palm print can be possible with both low resolution and high resolution images. Low resolution images are more appropriate for commercial and civil applications such as financial transaction, access control, etc. while High resolution images are appropriate for forensic applications such as criminal detection. Generally speaking, high resolution rises to four hundred dpi or more while low resolution rises to one hundred and fifty dpi or less. Researchers can extract generally principal lines, wrinkles and texture in low resolution while from high resolution images features are extracted as ridges, singular points and minutia points. In this paper a comparative study for palm print features of different subspace methods have been projected. Where the different subspace methods are separately exploited by using a classifier-Euclidean distance to find the algorithm performance. The experiment results by using two palm print databases determine that the proposed method of class specific information with 2D-PCA, alternate 2DPCA, Kernel PCA and (2D*2D) PCA, Where in comparison to other algorithm (2D*2D) PCA method provides the better results and the recognition rate by this method is given around 87 percent.
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
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.
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.
IJERT-A Review Paper Based on Palmprint Recognition System
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-review-paper-based-on-palmprint-recognition-system https://www.ijert.org/research/a-review-paper-based-on-palmprint-recognition-system-IJERTV3IS20846.pdf A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioural characteristic possessed by the user. Palmprint recognition being one of the important aspects of biometric technology is one of the most reliable and successful identification methods. It has four stages, palmprint image acquisition, preprocessing, feature extraction 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. Researchers have proposed a variety of palmprint preprocessing, feature extraction and matching approaches. This paper presents a detailed review of palmprint recognition approaches.
2018
Most biometric systems work by comparing features extracted from a query biometric trait with those extracted from a stored biometric trait. Therefore, to a great extent, the accuracy of any biometric system is dependent on the effectiveness of its features extraction stage. With an intention to establish a suitable appearance based features extraction technique, an independent comparative study of Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) algorithms for palmprint features extraction is reported in this article. Euclidean distance, Probabilistic Neural Network (PNN) and cosine measures were used as classifiers. Results obtained revealed that cosine metrics is preferable for ICA features extraction while PNN is preferable for LDA features extraction. Both PNN and Euclidean distance yielded a better recognition rate for PCA. However, ICA yielded the best recognition rate in terms of FAR and FRR followed by LDA then ...
Palm Print Recognition Based on Subspace Approaches
In todays world, automatic personal recognition is a crucial problem that needs to be solved properly. Palm print recognition is one of the most reliable and successful biometric solutions due to its numerous advantages such as stable line features, low resolution imaging, low cost capturing device, and user friendly. In this article, performance comparisons of palm print recognition techniques based on subspace approaches (PCA and 2DPCA) have presented. The experimental results are evaluated on three benchmark databases (CASIA, Cropped palm images and IIT Delhi) in terms of recognition rate and computation time.