221 results found
Liwicki S, Zafeiriou S, Pantic M, 2013, Incremental slow feature analysis with indefinite kernel for online temporal video segmentation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 7725 LNCS, Pages: 162-176, ISSN: 0302-9743
Slow Feature Analysis (SFA) is a subspace learning method inspired by the human visual system, however, it is seldom seen in computer vision. Motivated by its application for unsupervised activity analysis, we develop SFA's first implementation of online temporal video segmentation to detect episodes of motion changes. We utilize a domain-specific indefinite kernel which takes the data representation into account to introduce robustness. As our kernel is indefinite (i.e. defines instead of a Hilbert, a Krein space), we formulate SFA in Krein space. We propose an incremental kernel SFA framework which utilizes the special properties of our kernel. Finally, we employ our framework to online temporal video segmentation and perform qualitative and quantitative evaluation. © 2013 Springer-Verlag.
Tzimiropoulos G, Alabort-I-Medina J, Zafeiriou S, et al., 2013, Generic active appearance models revisited, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 7726 LNCS, Pages: 650-663, ISSN: 0302-9743
The proposed Active Orientation Models (AOMs) are generative models of facial shape and appearance. Their main differences with the well-known paradigm of Active Appearance Models (AAMs) are (i) they use a different statistical model of appearance, (ii) they are accompanied by a robust algorithm for model fitting and parameter estimation and (iii) and, most importantly, they generalize well to unseen faces and variations. Their main similarity is computational complexity. The project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm which is admittedly the fastest algorithm for fitting AAMs. We show that not only does the AOM generalize well to unseen identities, but also it outperforms state-of-the-art algorithms for the same task by a large margin. Finally, we prove our claims by providing Matlab code for reproducing our experiments ( http://ibug.doc.ic.ac.uk/resources ). © 2013 Springer-Verlag.
Sagonas C, Tzimiropoulos G, Zafeiriou S, et al., 2013, A semi-automatic methodology for facial landmark annotation, 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 896-903, ISSN: 2160-7508
Bousmalis K, Zafeiriou S, Morency L-P, et al., 2013, Infinite Hidden Conditional Random Fields for Human Behavior Analysis, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 24, Pages: 170-177, ISSN: 2162-237X
Zafeiriou S, Atkinson GA, Hansen MF, et al., 2013, Face Recognition and Verification Using Photometric Stereo: The Photoface Database and a Comprehensive Evaluation, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, Vol: 8, Pages: 121-135, ISSN: 1556-6013
Zafeiriou L, Nicolaou MA, Zafeiriou S, et al., 2013, Learning Slow Features for Behaviour Analysis
Kotsia I, Zafeiriou S, Fotopoulos S, 2013, Affective Gaming: A Comprehensive Survey, 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 663-670, ISSN: 2160-7508
Marras I, Medina JA, Tzimiropoulos G, et al., 2013, Online Learning and Fusion of Orientation Appearance Models for Robust Rigid Object Tracking, 2013 10TH IEEE INTERNATIONAL CONFERENCE AND WORKSHOPS ON AUTOMATIC FACE AND GESTURE RECOGNITION (FG), ISSN: 2326-5396
Panagakis Y, Nicolaou MA, Zafeiriou S, et al., 2013, Robust Canonical Time Warping for the Alignment of Grossly Corrupted Sequences, 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 540-547, ISSN: 1063-6919
Zafeiriou S, Kotsia I, 2013, On One-Shot Kernels: Explicit Feature Maps and Properties
Nicolaou MA, Zafeiriou S, Pantic M, 2013, Correlated-Spaces Regression for Learning Continuous Emotion Dimensions
Sandbach G, Zafeiriou S, Pantic M, et al., 2012, Static and dynamic 3D facial expression recognition: A comprehensive survey, IMAGE AND VISION COMPUTING, Vol: 30, Pages: 683-697, ISSN: 0262-8856
Liwicki S, Zafeiriou S, Tzimiropoulos G, et al., 2012, Efficient Online Subspace Learning With an Indefinite Kernel for Visual Tracking and Recognition, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 23, Pages: 1624-1636, ISSN: 2162-237X
Minkov K, Zafeiriou S, Pantic M, 2012, A comparison of different features for automatic eye blinking detection with an application to analysis of deceptive behavior, 5th International Symposium on Communications Control and Signal Processing, ISCCSP 2012
Systems for robust and real-time detection and measuring of the duration of eye blinking have been widely used in many different applications. Initially, eye blinks were used to provide an input modality to people with severe disabilities so as to be able to access Human Computer Interaction (HCI) systems. Recently, eye-blinking behavior was used as an indicator of deceptive behavior. In this paper we present a comparison of different features such as raw-image intensities, the magnitude of the responses of Gabor filters, Histograms of Oriented Gradients (HOGs) and optical flow extracted to be used for automatic eye blinking detection. For the classification of the above mentioned features we employed Support Vector Machines (SVMs) with Gaussian Radial Basis Function (RBF) kernels. We assessed their performance using eye samples from a controversial TV show recently aired, the so-called Moment of Truth. In the end, we used the eye-blinking algorithms that provided the best results in order to analyze the eye-blinking behavior in cases in which the player answered truthfully and in cases he/she lied. © 2012 IEEE.
Tzimiropoulos G, Zafeiriou S, Pantic M, 2012, Subspace learning from image gradient orientations, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 34, Pages: 2454-2466, ISSN: 0162-8828
We introduce the notion of subspace learning from image gradient orientations for appearance-based object recognition. As image data are typically noisy and noise is substantially different from Gaussian, traditional subspace learning from pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing pixel intensities with gradient orientations and the ℓ2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Within this framework, which we coin Image Gradient Orientations (IGO) subspace learning, we first formulate and study the properties of Principal Component Analysis of image gradient orientations (IGO-PCA). We then show its connection to previously proposed robust PCA techniques both theoretically and experimentally. Finally, we derive a number of other popular subspace learning techniques, namely, Linear Discriminant Analysis (LDA), Locally Linear Embedding (LLE), and Laplacian Eigenmaps (LE). Experimental results show that our algorithms significantly outperform popular methods such as Gabor features and Local Binary Patterns and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition. In addition to this, the proposed IGO-methods require the eigendecomposition of simple covariance matrices and are as computationally efficient as their corresponding ℓ2 norm intensity-based counterparts. Matlab code for the methods presented in this paper can be found at http://ibug.doc.ic.ac.uk/resources.
Zafeiriou S, Tzimiropoulos G, Petrou M, et al., 2012, Regularized kernel discriminant analysis with a robust kernel for face recognition and verification, IEEE Transactions on Neural Networks and Learning Systems, Vol: 23, Pages: 526-534, ISSN: 2162-237X
Sandbach G, Zafeiriou S, Pantic M, 2012, Binary Pattern Analysis for 3D Facial Action Unit Detection, PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2012
Marras I, Zafeiriou S, Tzimiropoulos G, 2012, Robust Learning from Normals for 3D Face Recognition, 12th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 230-239, ISSN: 0302-9743
Zafeiriou S, 2012, Subspace Learning in Krein Spaces: Complete Kernel Fisher Discriminant Analysis with Indefinite Kernels, 12th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 488-501, ISSN: 0302-9743
Sandbach G, Zafeiriou S, Pantic M, 2012, LOCAL NORMAL BINARY PATTERNS FOR 3D FACIAL ACTION UNIT DETECTION, 19th IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 1813-1816, ISSN: 1522-4880
Zafeiriou S, Petrou M, 2011, 2.5D Elastic graph matching, COMPUTER VISION AND IMAGE UNDERSTANDING, Vol: 115, Pages: 1062-1072, ISSN: 1077-3142
We introduce a fast and robust subspace-based approach to appearance-based object tracking. The core of our approach is based on Fast Robust Correlation (FRC), a recently proposed technique for the robust estimation of large translational displacements. We show how the basic principles of FRC can be naturally extended to formulate a robust version of Principal Component Analysis (PCA) which can be efficiently implemented incrementally and therefore is particularly suitable for robust real-time appearance-based object tracking. Our experimental results demonstrate that the proposed approach outperforms other state-of-the-art holistic appearance-based trackers on several popular video sequences. © 2011 IEEE.
Tzimiropoulos G, Zafeiriou S, Pantic M, 2011, Principal component analysis of image gradient orientations for face recognition, Pages: 553-558
We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data population. We show that replacing intensities with gradient orientations and the 2 norm with a cosine-based distance measure offers, to some extend, a remedy to this problem. Our scheme requires the eigen-decomposition of a covariance matrix and is as computationally efficient as standard 2 intensity-based PCA. We demonstrate some of its favorable properties for the application of face recognition. © 2011 IEEE.
Sandbach G, Zafeiriou S, Pantic M, et al., 2011, A dynamic approach to the recognition of 3D facial expressions and their temporal models, Pages: 406-413
In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modeled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order to extract features for each of the onset and offset segments of the expression. These features are then used to train a Hidden Markov Model in order to model the full temporal dynamics of the expression. The proposed fully automatic system was tested in a subset of the BU-4DFE database for the recognition of happiness, anger and surprise. Comparisons with a similar system based on the motion extracted from facial intensity images was also performed. The attained results suggest that the use of the 3D information does indeed improve the recognition accuracy when compared to the 2D data. © 2011 IEEE.
Zafeiriou S, Petrou M, 2011, Nonnegative tensor factorization as an alternative Csiszar-Tusnady procedure: algorithms, convergence, probabilistic interpretations and novel probabilistic tensor latent variable analysis algorithms, DATA MINING AND KNOWLEDGE DISCOVERY, Vol: 22, Pages: 419-466, ISSN: 1384-5810
Eyben F, Petridis S, Schuller B, et al., 2011, Audiovisual Classification of Vocal Outbursts in Human Conversation Using Long-Short-Term Memory Networks, Publisher: IEEE, Pages: 5844-5847
Tzimiropoulos G, Zafeiriou S, Pantic M, 2011, Sparse representations of image gradient orientations for visual recognition and tracking, Pages: 26-33, ISSN: 2160-7508
Recent results  have shown that sparse linear representations of a query object with respect to an overcomplete basis formed by the entire gallery of objects of interest can result in powerful image-based object recognition schemes. In this paper, we propose a framework for visual recognition and tracking based on sparse representations of image gradient orientations. We show that minimal 1 solutions to problems formulated with gradient orientations can be used for fast and robust object recognition even for probe objects corrupted by outliers. These solutions are obtained without the need for solving the extended problem considered in . We further show that low-dimensional embeddings generated from gradient orientations perform equally well even when probe objects are corrupted by outliers, which, in turn, results in huge computational savings. We demonstrate experimentally that, compared to the baseline method in , our formulation results in better recognition rates without the need for block processing and even with smaller number of training samples. Finally, based on our results, we also propose a robust and efficient ℓ1-based tracking by detection algorithm. We show experimentally that our tracker outperforms a recently proposed ℓ1-based tracking algorithm in terms of robustness, accuracy and speed. © 2011 IEEE.
In this paper we present a new database suitable for both 2D and 3D face recognition based on photometric stereo, the so-called Photoface database. The Photoface database was collected using a custom-made four-source photometric stereo device that could be easily deployed in commercial settings. Unlike other publicly available databases the level of cooperation between subjects and the capture mechanism was minimal. The proposed device may also be used, to capture 3D expressive faces. Apart from the description of the device and the Photoface database, we present experiments from baseline face recognition and verification algorithms using albedo, normals and the recovered depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in the pipeline of photometric stereo (i.e. normal field computation and depth map reconstruction methods) affect recognition/verification performance. © 2011 IEEE.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.