Comprehensive Evaluation of Appearance-Basedtechniques for Palmprint Features Extraction Usingprobabilistic Neural Network, Cosine Measures Andeuclidean Distance Classifiers (original) (raw)
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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 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.
International Journal of Software Engineering and Its Applications, 2016
Palmprint recognition system has been one promising biometric system used in Presence System. There are some methods to recognize the individual palmprint as well as to extract its feature. In this research, two recognition methods are compared, i.e., backpropagation neural network and similarity measure using Euclidean distance. While, for feature extraction, we implemented Principal Components Analysis (PCA) method. From the research, it can be concluded that from test results, the best recognition using backpropogation neural networks is 93.33% which is reached when parameters used are: 100 principal components, 1 hidden layer, and 75 neurons. While, implementation of similarity measure using Euclidean distance, the best recognition rate is 96.67% which is reached when 75 principal components are used. When considering the time consumed in recognition, the Euclidean distance gives the better result, i.e. 17.09 seconds, while using backpropagation neural network with 75 neurons, time consumed is 425 seconds. Therefore, from this research, recognition implementation combining both PCA and Euclidean distance are more suggested rather than using combination of PCA and backpropagation neural network.
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 with PCA and ICA
2003
Palmprint is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich texture information of palmprint offers one of the powerful means in personal recognition. According to psycho-physiology study, the primary visual cortex in the visual area of human brain is responsible for creating the basis of a three-dimensional map of visual space, and extracting features about the form and orientation of objects. The basic model can be expressed as a linear superposition of basis functions. This idea inspired us to implement two well known linear projection techniques, namely Principle Component Analysis (PCA) and Independent Component Analysis (ICA) to extract the palmprint texture features. Two different frameworks of ICA [1] are adopted to compare with PCA for the recognition performances by using three different classification techniques. Framework I observed images as random variables and the pixels as outcomes while framework II treated pixels as random variables and the images as outcome. We are able to show that ICA framework II yields the best performance for identifying palmprints and it is able to provide both False Acceptance Rate (FAR) and False Rejection Rate (FRR) as low as 1%.
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
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.
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 Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images
In this advanced decade, automatic identification of individuals is a significant achievement due to the high demand of security system. Hence, individual recognition using biometrics data is leading in the field of image processing. Although biometrics data analysis using thumb impression and finger-prints are very popular since many years, sometimes it leads to false acceptance and rejection if any physical change occurs in the finger ridges. There may be a high risk of hacking the biometrics data which is now a big challenge for cyber security employees. This paper captures the palm-print images of individuals as referred biometrics data for individual recognition. The research work is based on one of the prior issue that is feature extraction to extract the features of palm-print image such as principle lines, textures, ridges and pores etc. For this, some of the feature extraction techniques such as Derivatives of Gaussian filter (DoG), Discrete Cosine Transform (DCT), Fast Fou...
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.