{"title":"Subspace learning with frequency regularizer: Its application to face recognition","authors":"Zhen Lei, Dong Yi, Xiangsheng Huang, S. Li","doi":"10.1109/ICB.2015.7139113","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139113","url":null,"abstract":"Subspace learning is an important technique to enhance the discriminative ability of feature representation and reduce the dimension to improve its efficiency. Due to limited training samples and the usual high-dimensional feature, subspace learning always suffers from overfitting problem, which affects its generalization performance. One possible method is to introduce prior information as a regularizer to constrain its solution space. Traditional regularizers are usually designed in spatial domain, which usually make the projection smooth. In this work, we propose a frequency regularizer (FR), which suppresses the high frequency energy so that the smooth priori is incorporated. Two representative supervised subspace methods with frequency regularizer, FR-LDA and FR-SR are introduced and further applied to face recognition problem. Extensive experiments on popular face databases validate the effectiveness and superiority of FR based subspace learning compared to traditional subspace learning methods.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755449","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}
{"title":"Improving feature based dorsal hand vein recognition through Random Keypoint Generation and fine-grained matching","authors":"Renke Zhang, Di Huang, Yiding Wang, Yunhong Wang","doi":"10.1109/ICB.2015.7139057","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139057","url":null,"abstract":"Recently, SIFT-like approaches have shown their advantages of performance and robustness in dorsal hand vein recognition. This paper presents a novel method to recognize the vein pattern of the dorsal hand, which discusses two important issues in the SIFT-like framework, i.e. keypoint detection and matching. For the former, a Gaussian Distribution based Random Keypoint Generation method (GDRKG) is proposed to localize a sufficient set of distinctive keypoints, which largely reduces the computational complexity of the state of the art ones, such as DoG, Harris, and Hessian. For the latter, a Multi-task Sparse Representation Classifier (MtSRC) based fine-grained matching strategy is introduced instead of traditional coarse-grained matching, to precisely measure the similarity between the feature sets of the samples. The proposed method is tested on a dataset of 2040 vein images of 204 dorsal hands, and it outperforms the state of the arts clearly proving its effectiveness.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116723665","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}
{"title":"Vectorized fingerprint representation using Minutiae Relation Code","authors":"N. Abe, Takashi Shinzaki","doi":"10.1109/ICB.2015.7139103","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139103","url":null,"abstract":"Minutiae-based vector representation algorithms have been proposed, which allow us not only to speed up matching tasks, but also to easily apply for various template protection techniques, such as Fuzzy Vault, Fuzzy Commitment, and BioHashing. In this paper, we propose a new vectorized fingerprint descriptor called Minutiae Relation Code(MRC), which consists of a set of vector-represented minutiae relation information between arbitrary minutiae. We also evaluate authentication performances using FVC2002 Database(DB1, DB2, DB3, DB4) and we show 0.82% Equal Error Rate(EER) in DB1, 0.82% in DB2, 2.71% in DB3, and 1.49% in DB4.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584074","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}
Lingfeng Zhang, Pengfei Dou, S. Shah, I. Kakadiaris
{"title":"Hierarchical multi-label framework for robust face recognition","authors":"Lingfeng Zhang, Pengfei Dou, S. Shah, I. Kakadiaris","doi":"10.1109/ICB.2015.7139086","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139086","url":null,"abstract":"In this paper, we propose a patch based face recognition framework. First, a face image is iteratively divided into multi-level patches and assigned hierarchical labels. Second, local classifiers are built to learn the local prediction of each patch. Third, the hierarchical relationships defined between local patches are used to obtain the global prediction of each patch. We develop three ways to learn the global prediction: majority voting, ℓ1-regularized weighting, and decision rule. Last, the global predictions of different levels are combined as the final prediction. Experimental results on different face recognition tasks demonstrate the effectiveness of our method.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117324038","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}
{"title":"Latent orientation field estimation via convolutional neural network","authors":"Kai Cao, Anil K. Jain","doi":"10.1109/ICB.2015.7139060","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139060","url":null,"abstract":"The orientation field of a fingerprint is crucial for feature extraction and matching. However, estimation of orientation fields in latents is very challenging because latents are usually of poor quality. Inspired by the superiority of convolutional neural networks (ConvNets) for various classification and recognition tasks, we pose latent orientation field estimation in a latent patch to a classification problem, and propose a ConvNet based approach for latent orientation field estimation. The underlying idea is to identify the orientation field of a latent patch as one of a set of representative orientation patterns. To achieve this, 128 representative orientation patterns are learnt from a large number of orientation fields. For each orientation pattern, 10,000 fingerprint patches are selected to train the ConvNet. To simulate the quality of latents, texture noise is added to the training patches. Given image patches extracted from a latent, their orientation patterns are predicted by the trained ConvNet and quilted together to estimate the orientation field of the whole latent. Experimental results on NIST SD27 latent database demonstrate that the proposed algorithm outperforms the state-of-the-art orientation field estimation algorithms and can boost the identification performance of a state-of-the-art latent matcher by score fusion.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133469813","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}
{"title":"Sparse support faces","authors":"B. Biggio, Marco Melis, G. Fumera, F. Roli","doi":"10.1109/ICB.2015.7139053","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139053","url":null,"abstract":"Many modern face verification algorithms use a small set of reference templates to save memory and computational resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well-principled approach, named sparse support faces, that can outperform state-of-the-art methods both in terms of recognition accuracy and number of required face templates, by jointly learning an optimal combination of matching scores and the corresponding subset of face templates. For each client, our method learns a support vector machine using the given matching algorithm as the kernel function, and determines a set of reference templates, that we call support faces, corresponding to its support vectors. It then drastically reduces the number of templates, without affecting recognition accuracy, by learning a set of virtual faces as well-principled transformations of the initial support faces. The use of a very small set of support face templates makes the decisions of our approach also easily interpretable for designers and end users of the face verification system.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993393","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}
M. Gomez-Barrero, Javier Galbally, Julian Fierrez, J. Ortega-Garcia, R. Plamondon
{"title":"Enhanced on-line signature verification based on skilled forgery detection using Sigma-LogNormal Features","authors":"M. Gomez-Barrero, Javier Galbally, Julian Fierrez, J. Ortega-Garcia, R. Plamondon","doi":"10.1109/ICB.2015.7139065","DOIUrl":"https://doi.org/10.1109/ICB.2015.7139065","url":null,"abstract":"One of the biggest challenges in on-line signature verification is the detection of skilled forgeries. In this paper, we propose a novel scheme, based on the Kinematic Theory of rapid human movements and its associated Sigma LogNormal model, to improve the performance of on-line signature verification systems. The approach combines the high performance of DTW-based systems in verification tasks, with the high potential for skilled forgery detection of the Kinematic Theory of rapid human movements. Experiments were carried out on the publicly available BiosecurID multimodal database, comprising 400 subjects. Results show that the performance of the DTW-based system improves for both skilled and random forgeries.","PeriodicalId":237372,"journal":{"name":"2015 International Conference on Biometrics (ICB)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372828","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}