Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094307
Azadeh Ghandehari, R. Safabakhsh
This paper investigates palmprint recognition using Principal Component Analysis (PCA) and the Adaptive Principal component EXtraction (APEX) which is one of the PCA techniques involving neural network. Through implementing the PCA and APEX algorithms for extracting features and applying them to palmprint recognition with two classifiers, Euclidean distance and Hamming distance, it was made known that APEX algorithm is efficient in palmprint recognition and the rate of recognition given by APEX is way more than PCA.
{"title":"A Comparison of Principal Component Analysis and Adaptive Principal Component Extraction for Palmprint Recognition","authors":"Azadeh Ghandehari, R. Safabakhsh","doi":"10.1109/ICHB.2011.6094307","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094307","url":null,"abstract":"This paper investigates palmprint recognition using Principal Component Analysis (PCA) and the Adaptive Principal component EXtraction (APEX) which is one of the PCA techniques involving neural network. Through implementing the PCA and APEX algorithms for extracting features and applying them to palmprint recognition with two classifiers, Euclidean distance and Hamming distance, it was made known that APEX algorithm is efficient in palmprint recognition and the rate of recognition given by APEX is way more than PCA.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132484972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094308
Shahla Saedi, Nasrollah Moghadam Charkari
In this paper, we propose a novel and efficient texture based approach to palmprint recognition based on a 2D discrete orthonormal S-Transform called as the 2D-DOST. 2D-DOST is a new powerful tool for texture analysis which can effectively extract the frequency contribution of image texture. In this work, First 2D-DOST is applied to the palmprint to characterize the frequency content of palmprint texture. Then, the local energy of 2D-DOST magnitudes in different bandwidths are computed and regarded as palmprint features. In the experiments, three databases, namely, CASIA, PolyU and IITD databases, are used to evaluate the performance of the proposed method. Also, The performance of 2D-DOST method is evaluated based on a set of different similarity/dissimilarity measures. The experimental results offer ERR equal to 0.93%, 0.97% and 0.12% for IITD, CASIA and PolyU databases, respectively which demonstrate the efficiency and validity of the proposed method.
{"title":"Characterization of Palmprint Using Discrete Orthonormal S-Transform","authors":"Shahla Saedi, Nasrollah Moghadam Charkari","doi":"10.1109/ICHB.2011.6094308","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094308","url":null,"abstract":"In this paper, we propose a novel and efficient texture based approach to palmprint recognition based on a 2D discrete orthonormal S-Transform called as the 2D-DOST. 2D-DOST is a new powerful tool for texture analysis which can effectively extract the frequency contribution of image texture. In this work, First 2D-DOST is applied to the palmprint to characterize the frequency content of palmprint texture. Then, the local energy of 2D-DOST magnitudes in different bandwidths are computed and regarded as palmprint features. In the experiments, three databases, namely, CASIA, PolyU and IITD databases, are used to evaluate the performance of the proposed method. Also, The performance of 2D-DOST method is evaluated based on a set of different similarity/dissimilarity measures. The experimental results offer ERR equal to 0.93%, 0.97% and 0.12% for IITD, CASIA and PolyU databases, respectively which demonstrate the efficiency and validity of the proposed method.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133587801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094304
Feng Yue, Bin Li, Ming Yu, Jiaqiang Wang
A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. In this paper, by viewing the palmprint feature as a high-dimension binary vector, we present a palmprint identification method using orientation pattern hashing. We propose three properties required by the hash function and demonstrate that the orientation pattern has all of these properties. Under some simple assumptions we give the parameter selection method for fast and accurate palmprint identification. Experimental results on the Hong Kong large scale database (9667 palms) show that the proposed method is over 16 times faster than brute force searching, while its accuracy is slightly higher. Evaluations on the CASIA palmprint database (600 palms) plus a synthetic database (100,000 palms) show a speedup of 6.8 over brute force searching and a negligible loss of accuracy.
{"title":"Fast Palmprint Identification Using Orientation Pattern Hashing","authors":"Feng Yue, Bin Li, Ming Yu, Jiaqiang Wang","doi":"10.1109/ICHB.2011.6094304","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094304","url":null,"abstract":"A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. In this paper, by viewing the palmprint feature as a high-dimension binary vector, we present a palmprint identification method using orientation pattern hashing. We propose three properties required by the hash function and demonstrate that the orientation pattern has all of these properties. Under some simple assumptions we give the parameter selection method for fast and accurate palmprint identification. Experimental results on the Hong Kong large scale database (9667 palms) show that the proposed method is over 16 times faster than brute force searching, while its accuracy is slightly higher. Evaluations on the CASIA palmprint database (600 palms) plus a synthetic database (100,000 palms) show a speedup of 6.8 over brute force searching and a negligible loss of accuracy.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133225499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094332
Daniel Hartung, Sophie Martin, C. Busch
The quality of captured samples is a critical aspect in biometric systems. In this paper we present a quality estimation algorithm for vascular images, which uses global and local features based on a Grey Level Co-Occurrence Matrix (GLCM) and optionally available metadata. An evaluation of the algorithm using different processing methods and vein sample databases shows convincing results: disregarding low estimated quality sample images helps to increase the performance. Moreover, metadata gives accurate indications on sample quality. The algorithm works on low level raw images, it is fast and therefore qualified to be used in feedback mode during enrolment or verification operation.
{"title":"Quality Estimation for Vascular Pattern Recognition","authors":"Daniel Hartung, Sophie Martin, C. Busch","doi":"10.1109/ICHB.2011.6094332","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094332","url":null,"abstract":"The quality of captured samples is a critical aspect in biometric systems. In this paper we present a quality estimation algorithm for vascular images, which uses global and local features based on a Grey Level Co-Occurrence Matrix (GLCM) and optionally available metadata. An evaluation of the algorithm using different processing methods and vein sample databases shows convincing results: disregarding low estimated quality sample images helps to increase the performance. Moreover, metadata gives accurate indications on sample quality. The algorithm works on low level raw images, it is fast and therefore qualified to be used in feedback mode during enrolment or verification operation.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125088502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094306
Hao Li, Zhenhua Guo, Shouyu Ma, Nan Luo
Palmprint image has been widely used in personal recognition system, and various techniques have been proposed to improve its performance. It is known that the recognition accuracy depends on the location effect to a great extent. In this paper, we proposed a new method in locating the central block of palmprint image, which makes use of the distance between corner points and contour centroid. This method aims to achieve a faster and more stable location on palmprint image. And it can tolerate the rotation of palmprint image in plane surface. This system consists of two parts: a novel device for touchless palmprint image acquisition and an efficient algorithm for palmprint location.
{"title":"A New Touchless Palmprint Location Method Based on Contour Centroid","authors":"Hao Li, Zhenhua Guo, Shouyu Ma, Nan Luo","doi":"10.1109/ICHB.2011.6094306","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094306","url":null,"abstract":"Palmprint image has been widely used in personal recognition system, and various techniques have been proposed to improve its performance. It is known that the recognition accuracy depends on the location effect to a great extent. In this paper, we proposed a new method in locating the central block of palmprint image, which makes use of the distance between corner points and contour centroid. This method aims to achieve a faster and more stable location on palmprint image. And it can tolerate the rotation of palmprint image in plane surface. This system consists of two parts: a novel device for touchless palmprint image acquisition and an efficient algorithm for palmprint location.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115837523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094354
Ruili Zhou, SangWoo Sin, Dongju Li, T. Isshiki, H. Kunieda
The performance of an fingerprint authentication algorithm can be decreased significantly if the fingerprint image has lots of broken ridges caused by cutline, or the overlap area between the template and input is very small. For the purpose of these specific kinds of verification, a Scale Invariant Feature Transformation (SIFT) feature-based algorithm for fingerprint verification is presented. This approach is not based on traditional minutiae or ridge features. The SIFT keypoints in Gaussian scale-space and the local descriptor for each SIFT keypoint can be extracted by using this method. The verification is done by matching the descriptor, which is invariant to image scale and rotation. In this paper a proper pre-processing is carried out on the fingerprint image instead of using the original fingerprint image. This can make the algorithm adaptive to the variation of the impression condition. Furthermore, a Hough transform adapted to fingerprint verification is performed rather than only using SIFT keypoint descriptor matching. The fusion with minutiae information is also applied for efficiency and accuracy. Two specific databases are captured for experiments. Experiment results of proposed algorithm on specific databases show significant improvement compared with common minutiae-based method. Experiment results on FVC2002 Database show that Equal Error Rate (EER) and False Matching Rate (FMR) of our proposed algorithm can be decreased to about 20% of previous SIFT-based works.
{"title":"Adaptive SIFT-Based Algorithm for Specific Fingerprint Verification","authors":"Ruili Zhou, SangWoo Sin, Dongju Li, T. Isshiki, H. Kunieda","doi":"10.1109/ICHB.2011.6094354","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094354","url":null,"abstract":"The performance of an fingerprint authentication algorithm can be decreased significantly if the fingerprint image has lots of broken ridges caused by cutline, or the overlap area between the template and input is very small. For the purpose of these specific kinds of verification, a Scale Invariant Feature Transformation (SIFT) feature-based algorithm for fingerprint verification is presented. This approach is not based on traditional minutiae or ridge features. The SIFT keypoints in Gaussian scale-space and the local descriptor for each SIFT keypoint can be extracted by using this method. The verification is done by matching the descriptor, which is invariant to image scale and rotation. In this paper a proper pre-processing is carried out on the fingerprint image instead of using the original fingerprint image. This can make the algorithm adaptive to the variation of the impression condition. Furthermore, a Hough transform adapted to fingerprint verification is performed rather than only using SIFT keypoint descriptor matching. The fusion with minutiae information is also applied for efficiency and accuracy. Two specific databases are captured for experiments. Experiment results of proposed algorithm on specific databases show significant improvement compared with common minutiae-based method. Experiment results on FVC2002 Database show that Equal Error Rate (EER) and False Matching Rate (FMR) of our proposed algorithm can be decreased to about 20% of previous SIFT-based works.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115797012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094318
Wenming Yang, Qing Rao, Q. Liao
Recent years have seen a plenty of personal identification methods with different biometrics such as finger pattern, face, palm-print and vein. The majority of these methods focus on complex image data projections and transforms in Fourier space, wavelet space or other domains, which usually bring heavy load in computation and difficult understanding in perceptual intuition. Moreover, these methods, oriented to multiple samples learning, are constricted usually in application. Among so much biometrics, vein, as a living feature with high anti-counterfeiting capability, has attracted considerable attention. In this paper, we propose a structured personal identification approach using finger vein Location and Direction Coding(LDC). First of all, we design a finger vein imaging device with near-infrared(NIR) light source, by which a database for finger vein images is established. Subsequently, we make use of the brightness difference in the finger vein image to extract the vein pattern. Furthermore, finger vein LDC is proposed and performed, which creates a structured feature image for each finger vein. Finally, the structured feature image is utilized to conduct the personal identification on our image database for finger vein, which includes 440 vein images from 220 different fingers. The equal error rate of our method for this database is 0.44%.
{"title":"Personal Identification for Single Sample Using Finger Vein Location and Direction Coding","authors":"Wenming Yang, Qing Rao, Q. Liao","doi":"10.1109/ICHB.2011.6094318","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094318","url":null,"abstract":"Recent years have seen a plenty of personal identification methods with different biometrics such as finger pattern, face, palm-print and vein. The majority of these methods focus on complex image data projections and transforms in Fourier space, wavelet space or other domains, which usually bring heavy load in computation and difficult understanding in perceptual intuition. Moreover, these methods, oriented to multiple samples learning, are constricted usually in application. Among so much biometrics, vein, as a living feature with high anti-counterfeiting capability, has attracted considerable attention. In this paper, we propose a structured personal identification approach using finger vein Location and Direction Coding(LDC). First of all, we design a finger vein imaging device with near-infrared(NIR) light source, by which a database for finger vein images is established. Subsequently, we make use of the brightness difference in the finger vein image to extract the vein pattern. Furthermore, finger vein LDC is proposed and performed, which creates a structured feature image for each finger vein. Finally, the structured feature image is utilized to conduct the personal identification on our image database for finger vein, which includes 440 vein images from 220 different fingers. The equal error rate of our method for this database is 0.44%.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094330
R. R. Coelho, Daniel Ramos Coelho, Marina de Oliveira Costa Figueiredo, G. Cunha
The aim of this article is to describe the development of a low cost motion capture system to analyze the movement of the wrist-hand complex (WHC). First, we searched the relevant literature about resources that could be used to develop a system for capturing motion. Through this search, we opted for the use of a system integrating an electronic platform and sensors (flexsensor and stretchsensor) that are able to capture the movements of WHC. A program written in the Processing 1.2.1 language was developed to allow the interpretation of the information obtained by the sensors and the electronic platform by the computer. Then, we defined the type, size and position of each sensor to capture the movement of each single joint of the WHC and the forearm movements of prone/supine. The prototype system proved to be a viable tool to test movements of the WHC. The results indicated that the proposed system could be useful to analyze movements of the WHC. On the other hand, the necessity of a wire connection from the sensors to the electronic platform and from the platform to a computer, the small numbers of analogical input of the developed electronic platform, the mechanical fragility of the sensors, and difficulty to evaluate the complex movement of opposition of the fingers can be limitations to this system. Another limitation of the system is its calibration. To overcome this problem, we intent to use measures of angles instead of the electrical resistance of the sensors.
{"title":"Development of a Low Cost Motion Capture System to Analyze Wrist-Hand Complex","authors":"R. R. Coelho, Daniel Ramos Coelho, Marina de Oliveira Costa Figueiredo, G. Cunha","doi":"10.1109/ICHB.2011.6094330","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094330","url":null,"abstract":"The aim of this article is to describe the development of a low cost motion capture system to analyze the movement of the wrist-hand complex (WHC). First, we searched the relevant literature about resources that could be used to develop a system for capturing motion. Through this search, we opted for the use of a system integrating an electronic platform and sensors (flexsensor and stretchsensor) that are able to capture the movements of WHC. A program written in the Processing 1.2.1 language was developed to allow the interpretation of the information obtained by the sensors and the electronic platform by the computer. Then, we defined the type, size and position of each sensor to capture the movement of each single joint of the WHC and the forearm movements of prone/supine. The prototype system proved to be a viable tool to test movements of the WHC. The results indicated that the proposed system could be useful to analyze movements of the WHC. On the other hand, the necessity of a wire connection from the sensors to the electronic platform and from the platform to a computer, the small numbers of analogical input of the developed electronic platform, the mechanical fragility of the sensors, and difficulty to evaluate the complex movement of opposition of the fingers can be limitations to this system. Another limitation of the system is its calibration. To overcome this problem, we intent to use measures of angles instead of the electrical resistance of the sensors.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117035108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094319
Jaime Uriarte-Antonio, J. E. Suarez-Pascual, M. Garcia-Lorenz, R. Sánchez-Reillo
Abstract-In this paper, authors analyze the parameters of a vein pattern recognition system designed before and introduce some changes to improve the results obtained. This system is designed to be used with the user's hand or wrist, and the feature extraction is based on a minutiae extraction approach. The system has been tested with two different databases: one database acquired by authors (UC3M) and one semi-publicly accessible database (Singapore). Both databases will be used to analyze the algorithm parameters and to show the improvement of the performance achieved with the reference system.
{"title":"Parametrical Study of a Vascular Biometric System","authors":"Jaime Uriarte-Antonio, J. E. Suarez-Pascual, M. Garcia-Lorenz, R. Sánchez-Reillo","doi":"10.1109/ICHB.2011.6094319","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094319","url":null,"abstract":"Abstract-In this paper, authors analyze the parameters of a vein pattern recognition system designed before and introduce some changes to improve the results obtained. This system is designed to be used with the user's hand or wrist, and the feature extraction is based on a minutiae extraction approach. The system has been tested with two different databases: one database acquired by authors (UC3M) and one semi-publicly accessible database (Singapore). Both databases will be used to analyze the algorithm parameters and to show the improvement of the performance achieved with the reference system.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121013251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-12-05DOI: 10.1109/ICHB.2011.6094310
Nirmala Saini, A. Sinha
In the present paper, a palmprint recognition system has been proposed in which the recently proposed Gabor-Wigner transform (GWT) has been used to extract the features from the palmprint images. The novelty of the system lies in the fact that GWT has been used for the first time for feature extraction in a biometric system. A particle swarm optimization technique has been used to select the significant features and to reduce the dimension of the obtained feature vectors while keeping the same level of performance. The receiver operating characteristic (ROC) curve and the equal error rate (EER) have been used to evaluate the performance of the technique
{"title":"A Palmprint Recognition System Based on Gabor Wigner Transform as Feature Extraction Technique","authors":"Nirmala Saini, A. Sinha","doi":"10.1109/ICHB.2011.6094310","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094310","url":null,"abstract":"In the present paper, a palmprint recognition system has been proposed in which the recently proposed Gabor-Wigner transform (GWT) has been used to extract the features from the palmprint images. The novelty of the system lies in the fact that GWT has been used for the first time for feature extraction in a biometric system. A particle swarm optimization technique has been used to select the significant features and to reduce the dimension of the obtained feature vectors while keeping the same level of performance. The receiver operating characteristic (ROC) curve and the equal error rate (EER) have been used to evaluate the performance of the technique","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115808270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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