Incorporating PCA feature extraction and evaluating feature matching methods in Palm Print Based Biometric Verification System (original) (raw)
Related papers
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.
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 ...
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.
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
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.
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.
A Survey on Palmprint-Based Biometric Recognition System
Advances in Computational Intelligence and Robotics
The automatic use of physiological or behavioral characteristics to determine or verify identity of individual's is regarded as biometrics. Fingerprints, Iris, Voice, Face, and palmprints are considered as physiological biometrics whereas voice and signature are behavioral biometrics. Palmprint recognition is one of the popular methods which have been investigated over last fifteen years. Palmprint have very large internal surface and contain several unique stable characteristic features used to identify individuals. Several palmprint recognition methods have been extensively studied. This chapter is an attempt to review current palmprint research, describing image acquisition, preprocessing palmprint feature extraction and matching, palmprint related fusion and techniques used for real time palmprint identification in large databases. Various palmprint recognition methods are compared.
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%).
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.