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Multifeature Palmprint Recognitionusing Feature Level Fusion
Abstract and Figures
Palmprint verification is an important tool for authentication of an individual and it can be of significant value in security and e-commerce applications. Palmprint identification has gained high impact over the other biometric modalities due to its reliability and high user acceptance. This paper presents a palmprint based identification approach which uses the Gabor wavelet to extract multiple features available on the palmprint, by employing a feature level fusion and classified using nearest neighbor approach. Here, we extract the features using wavelet entropy consist of contrast, correlation, energy, and homogeneity. The features are fused at feature levels. Palmprint matching is then performed by using nearest neighbor classifier. We selected 25 individuals' left hand palm images every person is 5 and total is 125.Then we get every persons each palm images as a template (total 25).The remaining 100 are as the training samples. The experimental results achieve recognition accuracy for Gabor real part of 98.4%, FRR is 0.8% and FAR is 1.6%.And Recognition accuracy obtained for Gabor imaginary part of 97.63%, FRR is 0.8% and FAR is 2.4% on the publicly available database of Hong Kong Polytechnic University. Experimental evaluation using palmprint image databases clearly demonstrates the efficient recognition performance of the proposed algorithm compared with the conventional palmprint recognition algorithms.
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... Finally, it clustered the non-overlapping block histograms of compact multi-feature codes into a feature vector for feature representation. In addition, in [92], a palmprint identification method was proposed, which used the Gabor filters to encode multi-view palmprint features. Then, these features were fused into a feature vector at the feature level for final identification. ...
Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail.
... The evaluation was expressed in an equal error rate (EER), which was 0.011%. Gayathri and Ramamoorthy [25] proposed feature level fusion for palmprint authentication of an individual. The three features correlation, energy, and homogeneity are fused together, and tested on 125 publicly available palmprint database of Hong Kong Polytechnic University using nearest neighbor classifier. ...
This paper discusses a comprehensive review of the previous research in the field of the finger vein recognition system with a focus on finger vein enhancements and features extraction advances and shortcomings. It starts with a general introduction of the biometric system followed by detailed descriptions on finger vein identification, and its architecture archival of it, which includes image acquisition, preprocessing of the image, feature extraction, and vein matching. This study focuses on related work proposed by previous researchers, issues in the field that originated from the related work, and a discussion of each of the issues associated and the proposed solutions to each of them. Next a comprehensive discussion on the advances and shortcomings of the existing techniques based on the qualities, capturing device, database, and feature of that quality is presented. Accurate comparisons between existing techniques are presented as tables to make it easy for new researchers to come up with advances and drawbacks of each technique without spending time on all existing research in this area.
... The Euclidian distance between the test logos and stored logos is calculated and the minimum Euclidian distance amongst all is used to classify the logo image as a member of the class. Other researchers like [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35] and [36], have also used feature level fusion to improve recognition performance in multimodal biometric systems. Our analysis shows that feature level fusion has picked up interest from various researchers as compared to before when score level fusion and decision level fusion were the most commonly used. ...
... The features are: Energy, Contrast, Homogeneity and Correlation. in this paper obtained recognition accuracy for Gabor real part accuracy as 98.4 percentages with false acceptance rate 1.6% and false rejection rate 0.8%.Gabor imaginary part accuracy as 97.63 percentages with false acceptance rate 2.4% and false rejection rate 0.8% [6]. ...
... Biometrics uses a variety of techniques for identifying a person based on the certain physiological or behavioral attributes. These attributes include fingerprintJain et al. (1997;facial features Sonkamble andThool (2011);Liu and Wechsler (2001), retina and iris patterns Wildes (1997), speech patterns Chou (2000), hand geometry SanchezReillo et al. (2000)and palmprint Zhang and Shu (1999);Duta et al. (2002);You et al. (2002);Kong and Zhang (2002);Chen et al. (2001);Gayathri and Ramamoorthy et al. (2012a;2012b),Gayathri and Ramamoorthy et al. (2012c);Krishneswari andArumugam (2012) andHaralick (1979). Biometric features of human being have a unique excellence: It is very ambiguous to remember the lengthy passwords and PIN numbers but biometric passwords are readily available for quick reference for identification. ...
Abstract: Problem statement: Palmprint based biometric method has gained high impact over the
other biometric methods due to its ease of acquisition, reliability and high client acceptance. Multiple
feature extraction from image gives higher accuracy of the authentication system. Approach: This
study presents the palmprint based identification methodology which uses the Gabor wavelet entropy
to extract multiple features existing on the palm print, by using a feature level fusion using Dempster-
Shafer theory and are classified using nearest neighbor approach. A feature having the same vector
can be grouped together using wavelet transform. A different feature of image using wavelet can be
extracted. Some of the features that can be extracted using wavelet entropy consist of contrast,
correlation, energy and homogeneity. The features are fused at feature levels. Palmprint matching is
then performed by using the nearest neighbor classifier. Results and Conclusion: We selected 100
individuals’ left hand palm images; every person is 6 and the total is 600. Later we got every person
each palm image as a template (total 100). The remaining 500 were treated as the training samples.
The experimental results achieve recognition accuracy of 98.6% and interesting working point with
False Acceptance Rate (FAR) of = 0.03% and False Rejection Rate (FRR) of = 1.4% on the publicly
available database of The Hong Kong Polytechnic University. Experimental assessment using
palmprint image databases clearly validates the efficient recognition performance of the suggested
algorithm compared with the conventional palmprint recognition algorithms.
Biometric recognition is a way of recognizing people on the basis of their behavioral and physiological characteristics. Palm print recognition is a very popular biometric recognition method because of its stable line features, need of low cost capturing device, low resolution imaging and user friendliness. Palm print recognition has been area of interest for many researchers since last many years due to the unique and stable characteristics present in a Palm. Researchers have suggested various preprocessing, feature extraction and matching techniques for recognition of a Palm print. This paper discusses various stages of Palm print recognition and research work performed in field of Palm Print recognition.
In many real-life usages, single modal biometric systems
repeatedly face significant restrictions due to noise in sensed
data, spoof attacks, data quality, nonuniversality, and other
factors. However, single traits alone may not be able to meet
the increasing demand of high accuracy in today’s biometric
system.Multibiometric systems is used to increase the
performance that may not be possible using single biometrics.
In this paper we propose a novel feature level fusion that
combines the information to investigate whether the integration
of palmprint and iris biometric can achieve performance that
may not be possible using a single biometric technology.
Proposed system extracts Gabor texture from the preprocessed
palm print and iris images. The feature vectors attained from
different methods are in different sizes and the features from
equivalent image may be correlated. Therefore, we proposed
wavelet-based fusion techniques. Finally the feature vector is
matched with stored template using KNN classifier. The
proposed approach is authenticated for their accuracy on PolyU
palmprint database fused with IITK iris database of 125 users.
The experimental results demonstrated that the proposed
multimodal biometric system achieves a recognition accuracy
of 99.2% and with false rejection rate (FRR) of = 1.6%.
As a unique and reliable biometric, palmprint recognition has attracted various researchers and achieved great success. However, palmprint alone may not be able to meet the increasing demand of high accuracy in today's biometric system. The purpose of our paper is to inspect whether the integration of palmprint and fingerprint biometric can achieve performance that may not be possible using a single biometric technology. In this work, palmprint authentication system is classified into palmprint image acquisition, preprocessing, feature extraction, feature fusion and matching. Here we employed an improved preprocessing technique to enhance the equalization. We propose a feature fusion for palmprint and fingerprint authentication. Proposed system extracts multiple features like texture (Gabor), line and appearance (PCA) features from the preprocessed palm print and fingerprint images. The feature vectors obtained from different approaches are in different dimensions and also the features from same image may be correlated. Therefore, we proposed wavelet-based fusion techniques to fuse extracted features as it contains wavelet extensions and uses mean-max fusion method to overcome the problem of feature fusion. Finally the feature vector is matched with the stored template using KNN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 40 users. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 98.82% and with the false rejection rate (FRR) of = 2.5%.
Problem statement: Hand geometry contains relatively invariant features of an individual. Palmprint recognition is an efficient biometric solution for authentication system. The existence of several hand-based authentication commercial systems indicates the effectiveness of this type of biometric. Approach: We proposed a palmprint verification system using high order Zernike moment that was robust to rotation, translation and occlusion. Zernike moment was an efficient algorithm for representing the shape features of an image. The design consists of feature extraction and matching of image using high order Zernike moment. Zernike moments at high orders was calculated from the image and the image was classified using K-Nearest Neighborhood (KNN). The reason for using Zernike moment was that it was the best algorithm due to its orthogonality and rotation invariance property. Results and Conclusion: Computational cost can be reduced by detecting the common term of Zernike moment. Experiments and classifications have been performed using Hong Kong PolyU palm print database with 125 individuals left hand palm images; every person has 5 samples, totaling up to 625. We then get every persons palm images as a template (totaling 125). The remaining 500 are used as the training samples. The proposed palmprint authentication system achieves a recognition accuracy of 98% and interesting working point with False Acceptance Rate (FAR) of = 1.062% and False Rejection Rate (FRR) of = 0%. Experimental evaluation demonstrates the efficient recognition performance of the proposed algorithm compared with conventional palmprint recognition algorithms.
Problem statement: Biometrics based personal identification is as an effective method for automatically recognizing, a person's identity with high confidence. Palmprint is an essential biometric feature for use in access control and forensic applications. In this study, we present a multi feature extraction, based on edge detection scheme, applying Log Gabor filter to enhance image structures and suppress noise. Approach: A novel Feature-Similarity Indexing (FSIM) of image algorithm is used to generate the matching score between the original image in database and the input test image. Feature Similarity (FSIM) index for full reference (image quality assurance) IQA is proposed based on the fact that Human Visual System (HVS) understands an image mainly according to its low-level features. Results and Conclusion: The experimental results achieve recognition accuracy using canny and perwitt FSIM of 97.3227 and 94.718%, respectively, on the publicly available database of Hong Kong Polytechnic University. Totally 500 images of 100 individuals, 4 samples for each palm are randomly selected to train in this research. Then we get every person each palm image as a template (total 100). Experimental evaluation using palmprint image databases clearly demonstrates the efficient recognition performance of the proposed algorithm compared with the conventional palmprint recognition algorithms.
Problem statement: Biometrics based personal identification is as an effective method for automatically recognizing, a persons identity with high confidence. Palmprint is an essential biometric feature for use in access control and forensic applications. In this study, we present a multi feature extraction, based on edge detection scheme, applying Log Gabor filter to enhance image structures and suppress noise. Approach: A novel Feature-Similarity Indexing (FSIM) of image algorithm is used to generate the matching score between the original image in database and the input test image. Feature Similarity (FSIM) index for full reference (image quality assurance) IQA is proposed based on the fact that Human Visual System (HVS) understands an image mainly according to its low-level features. Results and Conclusion: The experimental results achieve recognition accuracy using canny and perwitt FSIM of 97.3227 and 94.718%, respectively, on the publicly available database of Hong Kong Polytechnic University. Totally 500 images of 100 individuals, 4 samples for each palm are randomly selected to train in this research. Then we get every person each palm image as a template (total 100). Experimental evaluation using palmprint image databases clearly demonstrates the efficient recognition performance of the proposed algorithm compared with the conventional palmprint recognition algorithms.
Biometrics identification is an emerging technology for solving security problems in our networked society. A new branch of biometric technology, palmprint recognition, whereby the lines and points can be extracted from our palm for personal identification was proposed several years ago [1-6]. In this paper, we implement the feature extraction technique applied to iris recognition [7] on low – resolution palmprint images. A 2-D Gabor filter is used to obtain the texture information and two palmprint images are compared in term of their hamming distance. The experimental results show that our method is effective.
Personal identification, using biometric features has been attracting much attention lately. This paper describes specific verification technology by making use of hand-print features. Two hand-based features, the finger-print and the palm-print are considered. In contrast to the existing approaches, our system employs ID hand-print signal to achieve effective personal identification. The system consists of two parts: a convenient device for hand-print image acquisition and an efficient algorithm for fast palm-print recognition. A robust and adaptive image coordinate system is defined to facilitate feature extraction. And a discrete wavelet zero-crossing encoding scheme is applied to hand-print feature extraction and representation. The experimental results demonstrate the effectiveness of the proposed system
Biometric computing offers an effective approach to identify personal identity by using individual's unique, reliable and stable physical or behavioral characteristics. This paper describes a new method to authenticate individuals based on palmprint identification and verification. Firstly, a comparative study of palmprint feature extraction is presented. The concepts of texture feature and interesting points are introduced to define palmprint features. A texture-based dynamic selection scheme is proposed to facilitate the fast search for the best matching of the sample in the database in a hierarchical fashion. The global texture energy, which is characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination, is used to guide the dynamic selection of a small set of similar candidates from the database at coarse level for further processing. An interesting point based image matching is performed on the selected similar patterns at fine level for the final confirmation. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
This paper investigates the feasibility of person identification based on feature points extracted from palmprint images. Our approach first extracts a set of feature points along the prominent palm lines (and the associated line orientation) from a given palmprint image. Next we decide if two palmprints belong to the same hand by computing a matching score between the corresponding sets of feature points of the two palmprints. The two sets of feature points/orientations are matched using our previously developed point matching technique which takes into account the non-linear deformations as well as the outlier points present in the two sets. The estimates of the matching score distributions for the genuine and imposter sets of palm pairs showed that palmprints have a good discrimination power. The overlap between the genuine and imposter distributions was found to be about 5%. Our preliminary results indicate that adding palmprint information may improve the identity verification provided by fingerprints in cases where fingerprint images cannot be properly acquired (e.g., due to dry skin).
As the first attempt of automatic personal identification by palmprint, in this paper, two novel characteristics, datum point invariance and line feature matching, are presented in palmprint verification. The datum points of palmprint, which have the remarkable advantage of invariable location, are defined and their determination using the directional projection algorithm is developed. Then, line feature extraction and line matching are proposed to detect whether a couple of palmprints are from the same palm. Various palmprint images have been tested to illustrate the effectiveness of the palmprint verification with both characteristics.
Biometrics identification is an emerging solution for solving security problems in our networked society. A new branch of biometric approach - palmprint technology, whereby the lines and points can be extracted from our palm for personal authentication, was proposed several years ago. In this paper, we develop a new feature extraction method based on low-resolution palmprint images. A 2-D Gabor filter is used to obtain the texture information and two palmprint images are compared in term of their hamming distance. The experimental results illustrate the effectiveness of our method. Author name used in this publication: Wai Kin Kong Author name used in this publication: David Zhang Biometrics Research Centre, Department of Computing