2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)最新文献_第3页

A new efficient and adaptive sclera recognition system 一种新的高效自适应巩膜识别系统

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015436

Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein

{"title":"A new efficient and adaptive sclera recognition system","authors":"Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein","doi":"10.1109/CIBIM.2014.7015436","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015436","url":null,"abstract":"In this paper an efficient and adaptive biometric sclera recognition and verification system is proposed. Sclera segmentation was performed by Fuzzy C-means clustering. Since the sclera vessels are not prominent, in order to make them clearly visible image enhancement was required. Adaptive histogram equalization, followed by a bank of Discrete Meyer Wavelet was used to enhance the sclera vessel patterns. Feature extraction was performed by, Dense Local Directional Pattern (D-LDP). D-LDP patch descriptors of each training image are used to form a bag of features; further Spatial Pyramid Matching was used to produce the final training model. Support Vector Machines (SVMs) are used for classification. The UBIRIS version 1 dataset was used here for experimentation of the proposed system. To investigate regarding sclera patterns adaptively with respect to change in environmental condition, population, data accruing technique and time span two different session of the mention dataset are utilized. The images in two sessions are different in acquiring technique, representation, number of individual and they were captured in a gap of two weeks. An encouraging Equal Error Rate (EER) of 3.95% was achieved in the above mention investigation.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132743429","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}

引用次数: 40

Multi-angle based lively sclera biometrics at a distance 远距离多角度动态巩膜生物识别

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015439

Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein

{"title":"Multi-angle based lively sclera biometrics at a distance","authors":"Abhijit Das, U. Pal, M. A. Ferrer-Ballester, M. Blumenstein","doi":"10.1109/CIBIM.2014.7015439","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015439","url":null,"abstract":"This piece of work proposes a liveliness based sclera eye biometric, validation and recognition technique at a distance. The images in this work are acquired by a digital camera in the visible spectrum at varying distance of about 1 meter from the individual. Each individual during registration as well as validation is asked to look straight and move their eye ball up, left and right keeping their face straight to incorporate liveliness of the data. At first the image is divided vertically into two halves and the eyes are detected in each half of the face image that is captured, by locating the eye ball by a Circular Hough Transform. Then the eye image is cropped out automatically using the radius of the iris. Next a C-means-based segmentation is used for sclera segmentation followed by vessel enhancement by the adaptive histogram equalization and Haar filtering. The feature extraction was performed by patch-based Dense-LDP (Linear Directive Pattern). Furthermore each training image is used to form a bag of features, which is used to produce the training model. Each of the images of the different poses is combined at the feature level and the image level to obtain higher accuracy and to incorporate liveliness. The fusion that produces the best result is considered. Support Vector Machines (SVMs) are used for classification. Here images from 82 individuals (both left and right eye i.e. 164 different eyes) are used and an appreciable Equal Error Rate of 0.52% is achieved in this work.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115366063","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}

引用次数: 24

Differential evolution based score level fusion for multi-modal biometric systems 基于差分进化的多模态生物识别系统评分融合

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015441

Satrajit Mukherjee, Kunal Pal, Bodhisattwa Prasad Majumder, Chiranjib Saha, B. K. Panigrahi, Sanjoy Das

{"title":"Differential evolution based score level fusion for multi-modal biometric systems","authors":"Satrajit Mukherjee, Kunal Pal, Bodhisattwa Prasad Majumder, Chiranjib Saha, B. K. Panigrahi, Sanjoy Das","doi":"10.1109/CIBIM.2014.7015441","DOIUrl":"https://doi.org/10.1109/CIBIM.2014.7015441","url":null,"abstract":"The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion.","PeriodicalId":432938,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116354028","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}

引用次数: 9

Human body part detection using likelihood score computations 基于似然评分计算的人体部位检测

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015458

M. Ramanathan, W. Yau, E. Teoh

引用次数: 7

An automated multimodal biometric system and fusion 一个自动化的多模态生物识别系统和融合

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015438

Y. Kumar, A. Nigam, Kamlesh Tiwari, Phalguni Gupta

引用次数: 2

Gaze angle estimate and correction in iris recognition 虹膜识别中注视角估计与校正

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015454

Tao Yang, J. Stahl, S. Schuckers, Fang Hua, Chris Boehnen, M. Karakaya

引用次数: 4

Multi-spectral facial biometrics in access control 多光谱面部生物识别技术在门禁中的应用

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015450

K. Lai, S. Samoil, S. Yanushkevich

引用次数: 5

Handling session mismatch by fusion-based co-training: An empirical study using face and speech multimodal biometrics 基于融合的会话不匹配处理:基于人脸和语音多模态生物识别的实证研究

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 2014-12-01 DOI: 10.1109/CIBIM.2014.7015447

N. Poh, J. Kittler, A. Rattani

引用次数: 8

Robust face detection from still images 静态图像的鲁棒人脸检测

2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM) Pub Date : 1900-01-01 DOI: 10.1109/CIBIM.2014.7015446

Patrick Laytner, Chrisford Ling, Q. Xiao

引用次数: 13