16,338 research outputs found

    SSDL: Self-Supervised Domain Learning for Improved Face Recognition

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    Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual’s face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. The domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an “easy-to-hard” scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains

    ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

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    Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments

    Learning Robust Features for Gait Recognition by Maximum Margin Criterion

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    Extended abstract. The full research paper "Learning Robust Features for Gait Recognition by Maximum Margin Criterion" has been accepted for publication at the 23rd IEEE/IAPR International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico, December 2016

    String-like occluding region extraction for background restoration

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    The 18th International Conference on Pattern Recognition : ICPR 2006, slides ; Place : Hong Kong, China ; Date : August 20-24, 200

    La competició internacional de prototips basats en Microsoft Kinect premia uns investigadors espanyols‏

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    Investigadors del Centre de VisiĂł per Computador (CVC), la Universitat AutĂČnoma de Barcelona (UAB) i la Universitat de Barcelona (UB) han guanyat el tercer premi en el concurs internacional de demostradors organitzat per Microsoft Kinect i Texas Instruments al congrĂ©s International Conference on Pattern Recognition (ICPR) celebrat recentment a JapĂł i considerat un dels referents a nivell mundial en el reconeixement automĂ tic de patrons en imatges

    Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

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    Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-supervised variants have been proposed. Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account. In this work we compare traditional Expectation Maximization type approaches for semi-supervised linear discriminant analysis with approaches based on intrinsic constraints and propose a new principled approach for semi-supervised linear discriminant analysis, using so-called implicit constraints. We explore the relationships between these methods and consider the question if and in what sense we can expect improvement in performance over the supervised procedure. The constraint based approaches are more robust to misspecification of the model, and may outperform alternatives that make more assumptions on the data, in terms of the log-likelihood of unseen objects.Comment: 6 pages, 3 figures and 3 tables. International Conference on Pattern Recognition (ICPR) 2014, Stockholm, Swede

    Detection based low frame rate human tracking

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    Tracking by association of low frame rate detection responses is not trivial, as motion is less continuous and hence ambiguous. The problem becomes more challenging when occlusion occurs. To solve this problem, we firstly propose a robust data association method that explicitly differentiates ambiguous tracklets that are likely to introduce incorrect linking from other tracklets, and deal with them effectively. Secondly, we solve the long-time occlusion problem by detecting inter-track relationship and performing track split and merge according to appearance similarity and occlusion order. Experiment on a challenging human surveillance dataset shows the effectiveness of the proposed method. © 2010 IEEE.published_or_final_versionThe 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, 23-26 August 2010. In Proceedings of 20th ICPR, 2010, p. 3529-353

    Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures

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    In the field of gestural action recognition, many studies have focused on dimensionality reduction along the spatial axis, to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. It is noticeable that very few of these methods have explicitly addressed the dimensionality reduction along the time axis. This is however a major issue with regard to the use of elastic distances characterized by a quadratic complexity. To partially fill this apparent gap, we present in this paper an approach based on temporal down-sampling associated to elastic kernel machine learning. We experimentally show, on two data sets that are widely referenced in the domain of human gesture recognition, and very different in terms of quality of motion capture, that it is possible to significantly reduce the number of skeleton frames while maintaining a good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. The computational complexity reduction makes this approach eligible for real-time applications.Comment: ICPR 2014, International Conference on Pattern Recognition, Stockholm : Sweden (2014
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