When extracting discriminative features from multi-modal data, current methods rarely concern the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multi-modal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multi-modal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Two typical biometric data are considered in this paper for simplicity, i.e., face data and palmprint data. Compare with several representative multimodal biometrics recognition methods, the experimental results show that the proposed SDA-GSVD based multimodal biometric feature extraction approach achieves best recognition performance.
{"title":"Multi-Modal Biometric Feature Extraction and Recognition Based on Subclass Discriminant Analysis (SDA) and Generalized Singular Value Decomposition (GSVD)","authors":"Xiaoyuan Jing, Sheng Li, Yong-Fang Yao, Wen-Qian Li, Fei Wu, Chao Lan","doi":"10.1109/ICHB.2011.6094337","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094337","url":null,"abstract":"When extracting discriminative features from multi-modal data, current methods rarely concern the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multi-modal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multi-modal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Two typical biometric data are considered in this paper for simplicity, i.e., face data and palmprint data. Compare with several representative multimodal biometrics recognition methods, the experimental results show that the proposed SDA-GSVD based multimodal biometric feature extraction approach achieves best recognition performance.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"161 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":"133236633","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.2011.6094348
M. A. Medina-Pérez, Milton García-Borroto, A. E. Gutiérrez-Rodríguez, L. Altamirano-Robles
Fingerprint verification has become one of the most active research areas nowadays. A key component of an accurate fingerprint verification system is the fingerprint matching algorithm. An accurate matching algorithm uses a robust fingerprint representation. In this paper, we introduce m-triplets, a new minutiae triplet representation and similarity for fingerprint verification. The proposed similarity shifts the triplets to find the best minutiae correspondence and it uses rules that discard not matching minutiae triplets without comparing the whole representation; hence, achieving high matching speed. To test the quality of the introduced representation and similarity, we modify a popular fingerprint verification algorithm. The modified algorithm achieves the best accuracy and speed in all the databases of FVC2004 compared with four accurate verification algorithms.
{"title":"Robust Fingerprint Verification Using M-Triplets","authors":"M. A. Medina-Pérez, Milton García-Borroto, A. E. Gutiérrez-Rodríguez, L. Altamirano-Robles","doi":"10.1109/ICHB.2011.6094348","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094348","url":null,"abstract":"Fingerprint verification has become one of the most active research areas nowadays. A key component of an accurate fingerprint verification system is the fingerprint matching algorithm. An accurate matching algorithm uses a robust fingerprint representation. In this paper, we introduce m-triplets, a new minutiae triplet representation and similarity for fingerprint verification. The proposed similarity shifts the triplets to find the best minutiae correspondence and it uses rules that discard not matching minutiae triplets without comparing the whole representation; hence, achieving high matching speed. To test the quality of the introduced representation and similarity, we modify a popular fingerprint verification algorithm. The modified algorithm achieves the best accuracy and speed in all the databases of FVC2004 compared with four accurate verification algorithms.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"40 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":"131829632","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.2011.6094329
Shouyu Ma, Gang Wu, Naiwen Zhang, Hao Li, Nan Luo, QingWen Chen
Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the two dimensional (2D) image of the palm surface and use it for feature extraction and matching. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much three dimensional (3D) depth information is lost in the imaging process. In this paper, an embedded three dimensional surface measurement system based on digital signal processor (DSP) is presented. This embedded system is based on the principle of structure light. A group of coded stripes pictures are produced and projected by a configuration tool of DSP/BIOS operating system firstly. Then, phase information is unwrapped from capture images. Finally, cloud data is calculated. Experimental results show that this three- dimensional surface measurement embedded system based on DM642 can acquire three dimensional information efficiently and effectively
{"title":"Embedded Three-Dimensional Surface Measurement System for Palmprint","authors":"Shouyu Ma, Gang Wu, Naiwen Zhang, Hao Li, Nan Luo, QingWen Chen","doi":"10.1109/ICHB.2011.6094329","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094329","url":null,"abstract":"Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the two dimensional (2D) image of the palm surface and use it for feature extraction and matching. Although 2D palmprint recognition can achieve high accuracy, the 2D palmprint images can be easily counterfeited and much three dimensional (3D) depth information is lost in the imaging process. In this paper, an embedded three dimensional surface measurement system based on digital signal processor (DSP) is presented. This embedded system is based on the principle of structure light. A group of coded stripes pictures are produced and projected by a configuration tool of DSP/BIOS operating system firstly. Then, phase information is unwrapped from capture images. Finally, cloud data is calculated. Experimental results show that this three- dimensional surface measurement embedded system based on DM642 can acquire three dimensional information efficiently and effectively","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"84 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":"134457020","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.2011.6094314
Wei-qi Yuan, Lantao Jing
The main hand-shape features constantly used for identification are more than 10 kinds. The effects of the recognition performance are different for each feature. When few features with better specificity were selected for identification, the recognition accuracy could be close to that used all of the features. Meanwhile, the operation time and computing space could be reduced effectively. Thus, the paper purposed a method which chooses variance and recognition rate as the standard to evaluate the feature specificity and recognition performance for the feature selection. In the experiments, 11 features can be obtained from the images from 260 people's hands through the way of the artificial measurement. The specificity of each feature can be got independently by the standard of variance analysis. The matching experiment used the first 100 people's right-hand images. The more specific feature was saved in the eigenvector one by one, then, the recognition performance analysis could be done through the Euclidean distance. The experimental results showed that the recognition rate of the 3-feature eigenvector is 91.7%, and the 6-feature eigenvector is 94.2%. By contrast, the recognition rate reduced 2.5%, but the matching time reduced 0.5ms. Therefore, the 3 hand features of hand length, palm length and palm width can be used as part of the effective traits of the identification system, which can improve the speed of the recognition and can be easily integrated to the other biometric features.
{"title":"Hand-Shape Feature Selection and Recognition Performance Analysis","authors":"Wei-qi Yuan, Lantao Jing","doi":"10.1109/ICHB.2011.6094314","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094314","url":null,"abstract":"The main hand-shape features constantly used for identification are more than 10 kinds. The effects of the recognition performance are different for each feature. When few features with better specificity were selected for identification, the recognition accuracy could be close to that used all of the features. Meanwhile, the operation time and computing space could be reduced effectively. Thus, the paper purposed a method which chooses variance and recognition rate as the standard to evaluate the feature specificity and recognition performance for the feature selection. In the experiments, 11 features can be obtained from the images from 260 people's hands through the way of the artificial measurement. The specificity of each feature can be got independently by the standard of variance analysis. The matching experiment used the first 100 people's right-hand images. The more specific feature was saved in the eigenvector one by one, then, the recognition performance analysis could be done through the Euclidean distance. The experimental results showed that the recognition rate of the 3-feature eigenvector is 91.7%, and the 6-feature eigenvector is 94.2%. By contrast, the recognition rate reduced 2.5%, but the matching time reduced 0.5ms. Therefore, the 3 hand features of hand length, palm length and palm width can be used as part of the effective traits of the identification system, which can improve the speed of the recognition and can be easily integrated to the other biometric features.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"57 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":"127076413","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.2011.6094325
Z. S. Shariatmadar, K. Faez
Information fusion of various biometrics has attracted much attention in recent years. So in this paper we fused the information of biometrics in two different aspects. At the first, we investigate the information fusion in single modality, that is, the Finger-Knuckle-Print (FKP) biometric. FKP is one of the newest biometrics identifier which is recently used for personal identity authentication. For fusing the information of each FKP, two different representations of each image is used (Gray-Level intensity and its Gabor transform). On the other hand, two different subsets of feature vectors are extracted from each image. At the second stage, the information of each finger at two different fusion levels is fused: feature and matching score level. In fact this algorithm works as a kind of multi-modal method with a single biometric characteristic but multiple units. By fusing the information at different levels, the recognition rate can improve significantly. For example, by combining the information of four fingers, the recognition rate will be obtained 96.56% and 95.4% at feature and matching score levels, respectively. Poly-U Finger-Knuckle-Print database was used to examine the performance of the proposed method and the experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.
{"title":"An Efficient Method for Finger-Knuckle-Print Recognition by Using the Information Fusion at Different Levels","authors":"Z. S. Shariatmadar, K. Faez","doi":"10.1109/ICHB.2011.6094325","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094325","url":null,"abstract":"Information fusion of various biometrics has attracted much attention in recent years. So in this paper we fused the information of biometrics in two different aspects. At the first, we investigate the information fusion in single modality, that is, the Finger-Knuckle-Print (FKP) biometric. FKP is one of the newest biometrics identifier which is recently used for personal identity authentication. For fusing the information of each FKP, two different representations of each image is used (Gray-Level intensity and its Gabor transform). On the other hand, two different subsets of feature vectors are extracted from each image. At the second stage, the information of each finger at two different fusion levels is fused: feature and matching score level. In fact this algorithm works as a kind of multi-modal method with a single biometric characteristic but multiple units. By fusing the information at different levels, the recognition rate can improve significantly. For example, by combining the information of four fingers, the recognition rate will be obtained 96.56% and 95.4% at feature and matching score levels, respectively. Poly-U Finger-Knuckle-Print database was used to examine the performance of the proposed method and the experimental results demonstrated the efficiency and effectiveness of this new biometric characteristic.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"92 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":"125980499","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.2011.6094299
R. Raghavendra, B. Dorizzi
In this paper, we present an efficient feature selection scheme for biometric authentication (for both unimodal and multimodal systems) that allows selecting the dominant features and increase the performance of the overall system. More precisely, we propose an Adaptive Inertia Particle Swarm Optimization (AIPSO) algorithm such that the particle inertia weights are iteratively updated according to the particle fitness value. We then use AIPSO for selecting Log Gabor features for the face and palmprint modalities independently and on the fused Log Gabor space of these two modalities considered for fusion. Final classification (in both schemes) is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on 250 users selected from FRGC face database, PolyU palmprint database and a virtual person multimodal biometric database built from the considered face and palmprint databases. We compare the proposed selection method with well known feature selection schemes such as Sequential Floating Forward Selection (SFFS), Genetic Algorithm (GA), Adaptive Boosting (AdaBoost) and Normal PSO in terms of both number of features selected and performance. Experimental result results show better performance of our AIPSO compared to all other techniques with an improvement of around 5% in performance and a reduction of around 62% of features compared to the initial system (with full features).
{"title":"A Novel Adaptive Inertia Particle Swarm Optimization (AIPSO) Algorithm for Improving Multimodal Biometric Recognition","authors":"R. Raghavendra, B. Dorizzi","doi":"10.1109/ICHB.2011.6094299","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094299","url":null,"abstract":"In this paper, we present an efficient feature selection scheme for biometric authentication (for both unimodal and multimodal systems) that allows selecting the dominant features and increase the performance of the overall system. More precisely, we propose an Adaptive Inertia Particle Swarm Optimization (AIPSO) algorithm such that the particle inertia weights are iteratively updated according to the particle fitness value. We then use AIPSO for selecting Log Gabor features for the face and palmprint modalities independently and on the fused Log Gabor space of these two modalities considered for fusion. Final classification (in both schemes) is performed on the projection space of the selected features using Kernel Direct Discriminant Analysis (KDDA). Extensive experiments are carried out on 250 users selected from FRGC face database, PolyU palmprint database and a virtual person multimodal biometric database built from the considered face and palmprint databases. We compare the proposed selection method with well known feature selection schemes such as Sequential Floating Forward Selection (SFFS), Genetic Algorithm (GA), Adaptive Boosting (AdaBoost) and Normal PSO in terms of both number of features selected and performance. Experimental result results show better performance of our AIPSO compared to all other techniques with an improvement of around 5% in performance and a reduction of around 62% of features compared to the initial system (with full features).","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"2 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":"128934026","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.2011.6094321
Jinfeng Yang, Ben Zhang
In the near-infrared (NIR) light imaging manner, finger-vein images are always degraded greatly due to optical scattering in the biological tissue. This directly leads to difficulty in reliable finger-vein feature representation. To deal with the problem of finger-vein image degradation, this paper proposes a simple but effective image enhancement method based on scattering removal. First, according to the light propagation in biological tissue, a specific optical model is introduced to characterize the intensity of degraded finger-vein image as a linear combination of attenuation component and scattering component. By means of a regularization solution, the scattering component is then estimated to solve the optical model, and thereby the enhanced finger-vein image can be obtained. Finally, experimental results demonstrate the validity of the proposed method in contrast improvement for finger-vein images.
{"title":"Scattering Removal for Finger-Vein Image Enhancement","authors":"Jinfeng Yang, Ben Zhang","doi":"10.1109/ICHB.2011.6094321","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094321","url":null,"abstract":"In the near-infrared (NIR) light imaging manner, finger-vein images are always degraded greatly due to optical scattering in the biological tissue. This directly leads to difficulty in reliable finger-vein feature representation. To deal with the problem of finger-vein image degradation, this paper proposes a simple but effective image enhancement method based on scattering removal. First, according to the light propagation in biological tissue, a specific optical model is introduced to characterize the intensity of degraded finger-vein image as a linear combination of attenuation component and scattering component. By means of a regularization solution, the scattering component is then estimated to solve the optical model, and thereby the enhanced finger-vein image can be obtained. Finally, experimental results demonstrate the validity of the proposed method in contrast improvement for finger-vein images.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"293 1-2 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":"116861381","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.2011.6094313
Ester González, A. Morales, Miguel A. Ferrer, C. Travieso
Identification of people through hand based biometrics has been extensively researched by different scientific groups due to its simplicity, reliability, and acceptability. In fact, a great amount of proposal based on different procedures and acquisition devices has been published in the literature. However, the interoperability among them has been barely studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric systems. The experiments has been conducted on a database composed by 5400 hand images acquired with 6 different hand-shape biometric approaches including flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices including acquisitions on both sides of the hand. Our results suggest we are in the way to reach acceptable results of interoperability
{"title":"Looking for Hand Biometrics Interoperability","authors":"Ester González, A. Morales, Miguel A. Ferrer, C. Travieso","doi":"10.1109/ICHB.2011.6094313","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094313","url":null,"abstract":"Identification of people through hand based biometrics has been extensively researched by different scientific groups due to its simplicity, reliability, and acceptability. In fact, a great amount of proposal based on different procedures and acquisition devices has been published in the literature. However, the interoperability among them has been barely studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric systems. The experiments has been conducted on a database composed by 5400 hand images acquired with 6 different hand-shape biometric approaches including flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices including acquisitions on both sides of the hand. Our results suggest we are in the way to reach acceptable results of interoperability","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"12 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":"132366145","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.2011.6094322
Jinfeng Yang, Wanyin Wang
Finger-vein image enhancement is of great importance for finger-vein recognition since the quality of the finger-vein images always is very poor in practice. In this paper, a new method based on orientation field is proposed for reliable venous region enhancement. First, a coarse vein-width variation field (CVWVF) is adaptively estimated by the curvatures of the cross-sectional profiles in a finger-vein image. Second, a line filter transform (LFT) based on a line model with CVWVF constraint is computed for a primary orientation field (POF) generation in a finger-vein image. Third, to refine POF, a curve model with CVWVF constraint is used for implementing a curve filter transform (CFT). By CFT, the venous regions can be enhanced reliably in a finger-vein image. Finally, experimental results show that the proposed method has a good performance in finger-vein image enhancement.
{"title":"Finger-Vein Image Enhancement Based on Orientation Field","authors":"Jinfeng Yang, Wanyin Wang","doi":"10.1109/ICHB.2011.6094322","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094322","url":null,"abstract":"Finger-vein image enhancement is of great importance for finger-vein recognition since the quality of the finger-vein images always is very poor in practice. In this paper, a new method based on orientation field is proposed for reliable venous region enhancement. First, a coarse vein-width variation field (CVWVF) is adaptively estimated by the curvatures of the cross-sectional profiles in a finger-vein image. Second, a line filter transform (LFT) based on a line model with CVWVF constraint is computed for a primary orientation field (POF) generation in a finger-vein image. Third, to refine POF, a curve model with CVWVF constraint is used for implementing a curve filter transform (CFT). By CFT, the venous regions can be enhanced reliably in a finger-vein image. Finally, experimental results show that the proposed method has a good performance in finger-vein image enhancement.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"29 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":"128000978","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.2011.6094316
C. Kumar, Manimala S
The hands play a significant role in non-verbal communication. Hands may be affected by many disorders, most commonly traumatic injury. In treating hand problems, the mastery of anatomy is fundamental in order to provide the best quality of care. An attempt is made to predict all the geometric features of the hand only with the help of middle finger width. Geometric features of both the hands from 100 people of different age group were extracted from the silhouettes. The proposed method can be used to predict finger length, position of knuckles and also finger width at the first and second knuckle of all fingers using taalamana system and golden ratio. The estimation accuracy of more than 90% is achieved for all the estimated features of the hand except for thumb width which is 85%.
{"title":"Speculation of Hand Features from Middle Finger Width: A Novel Approach","authors":"C. Kumar, Manimala S","doi":"10.1109/ICHB.2011.6094316","DOIUrl":"https://doi.org/10.1109/ICHB.2011.6094316","url":null,"abstract":"The hands play a significant role in non-verbal communication. Hands may be affected by many disorders, most commonly traumatic injury. In treating hand problems, the mastery of anatomy is fundamental in order to provide the best quality of care. An attempt is made to predict all the geometric features of the hand only with the help of middle finger width. Geometric features of both the hands from 100 people of different age group were extracted from the silhouettes. The proposed method can be used to predict finger length, position of knuckles and also finger width at the first and second knuckle of all fingers using taalamana system and golden ratio. The estimation accuracy of more than 90% is achieved for all the estimated features of the hand except for thumb width which is 85%.","PeriodicalId":378764,"journal":{"name":"2011 International Conference on Hand-Based Biometrics","volume":"1 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":"128679634","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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