{"title":"On using gait biometrics to enhance face pose estimation","authors":"Sung-Uk Jung, M. Nixon","doi":"10.1109/BTAS.2010.5634473","DOIUrl":"https://doi.org/10.1109/BTAS.2010.5634473","url":null,"abstract":"Many face biometrics systems use controlled environments where subjects are viewed directly facing the camera. This is less likely to occur in surveillance environments, so a process is required to handle the pose variation of the human head, change in illumination, and low frame rate of input image sequences. This has been achieved using scale invariant features and 3D models to determine the pose of the human subject. Then, a gait trajectory model is generated to obtain the correct the face region whilst handing the looming effect. In this way, we describe a new approach aimed to estimate accurate face pose. The contributions of this research include the construction of a 3D model for pose estimation from planar imagery and the first use of gait information to enhance the face pose estimation process.","PeriodicalId":378536,"journal":{"name":"International Conference on Biometrics: Theory, Applications and Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128536813","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":"Multidimensional scaling for matching low-resolution facial images","authors":"S. Biswas, K. Bowyer, P. Flynn","doi":"10.1109/BTAS.2010.5634479","DOIUrl":"https://doi.org/10.1109/BTAS.2010.5634479","url":null,"abstract":"Face recognition performance degrades considerably when the input images are of poor resolution as is often the case for images taken by surveillance cameras or from a large distance. In this paper, we propose a novel approach for the recognition of low resolution images using multidimensional scaling. From a resolution point of view, the scenario yielding the best performance is when both the probe and gallery images are of high enough resolution to discriminate across different subjects. The proposed method embeds the low resolution images in an Euclidean space such that the distances between them in the transformed space approximates the best distances had both the images been of high resolution. The mapping is learned from high resolution training images and their corresponding low resolution images using iterative majorization algorithm. Extensive evaluation of the proposed approach on different datasets like PIE and FRGC with resolution as low as 7 × 6 pixels illustrates the usefulness of the method. We show that the proposed approach significantly improves the matching performance as compared to performing standard matching in the low-resolution domain. Performance comparison with different super-resolution techniques which obtains higher-resolution images prior to recognition further signifies the effectiveness of our approach.","PeriodicalId":378536,"journal":{"name":"International Conference on Biometrics: Theory, Applications and Systems","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125097190","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":"Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity","authors":"C. Rathgeb, A. Uhl, Peter Wild","doi":"10.1109/BTAS.2010.5634475","DOIUrl":"https://doi.org/10.1109/BTAS.2010.5634475","url":null,"abstract":"Daugman’s algorithm, mapping iris images to binary codes and estimating similarity between codes applying the fractional Hamming Distance, forms the basis of today’s commercially used iris recognition systems. However, when applied to large-scale databases, the linear matching of a single extracted iris-code against a gallery of templates is very time consuming and a bottleneck of current implementations. As an alternative to pre-screening techniques, our work is the first to present an incremental approach to iris recognition. We combine concentration of information in the first bits of an iris-code with early rejection of unlikely matches during matching stage to incrementally determine the best-matching candidate in the gallery. Our approach can transparently be applied to any iris-code based system and is able to reduce bit comparisons significantly (to about 5% of iris-code bits) while exhibiting a Rank-1 Recognition Rate being at least as high as for matches involving all bits.","PeriodicalId":378536,"journal":{"name":"International Conference on Biometrics: Theory, Applications and Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124281592","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}