Publications
218 results found
Nicolaou MA, Zafeiriou S, Pantic M, 2014, A unified framework for probabilistic component analysis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 8725 LNAI, Pages: 469-484, ISSN: 0302-9743
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministic component analysis methods. Under our framework, we unify many very popular and well-studied component analysis algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locality Preserving Projections (LPP) and Slow Feature Analysis (SFA), some of which have no probabilistic equivalents in literature thus far. We firstly define the Markov Random Fields (MRFs) which encapsulate the latent connectivity of the aforementioned component analysis techniques; subsequently, we show that the projection directions produced by all PCA, LDA, LPP and SFA are also produced by the Maximum Likelihood (ML) solution of a single joint probability density function, composed by selecting one of the defined MRF priors while utilising a simple observation model. Furthermore, we propose novel Expectation Maximization (EM) algorithms, exploiting the proposed joint PDF, while we generalize the proposed methodologies to arbitrary connectivities via parametrizable MRF products. Theoretical analysis and experiments on both simulated and real world data show the usefulness of the proposed framework, by deriving methods which well outperform state-of-the-art equivalents. © 2014 Springer-Verlag.
Zafeiriou L, Antonakos E, Zafeiriou S, et al., 2014, Joint unsupervised face alignment and behaviour analysis, 13th European Conference on Computer Vision (ECCV), Publisher: Springer International Publishing, Pages: 167-183, ISSN: 0302-9743
The predominant strategy for facial expressions analysis and temporal analysis of facial events is the following: a generic facial landmarks tracker, usually trained on thousands of carefully annotated examples, is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper challenges the above framework by showing that it is feasible to perform joint landmarks localization (i.e. spatial alignment) and temporal analysis of behavioural sequence with the use of a simple face detector and a simple shape model. To do so, we propose a new component analysis technique, which we call Autoregressive Component Analysis (ARCA), and we show how the parameters of a motion model can be jointly retrieved. The method does not require the use of any sophisticated landmark tracking methodology and simply employs pixel intensities for the texture representation.
Papaioannou A, Zafeiriou S, 2014, Principal Component Analysis With Complex Kernel: The Widely Linear Model, IEEE Transactions on Neural Networks and Learning Systems, Vol: 25, Pages: 1719-1726, ISSN: 2162-2388
Nonlinear complex representations, via the use of complex kernels, can be applied to model and capture the nonlinearities of complex data. Even though the theoretical tools of complex reproducing kernel Hilbert spaces (CRKHS) have been recently successfully applied to the design of digital filters and regression and classification frameworks, there is a limited research on component analysis and dimensionality reduction in CRKHS. The aim of this brief is to properly formulate the most popular component analysis methodology, i.e., Principal Component Analysis (PCA), in CRKHS. In particular, we define a general widely linear complex kernel PCA framework. Furthermore, we show how to efficiently perform widely linear PCA in small sample sized problems. Finally, we show the usefulness of the proposed framework in robust reconstruction using Euler data representation.
Argyriou V, Zafeiriou S, Petrou M, 2014, Optimal illumination directions for faces and rough surfaces for single and multiple light imaging using class-specific prior knowledge, COMPUTER VISION AND IMAGE UNDERSTANDING, Vol: 125, Pages: 16-36, ISSN: 1077-3142
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- Citations: 2
Antonakos E, Zafeiriou S, 2014, Automatic construction of deformable models in-the-wild, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Publisher: IEEE, Pages: 1813-1820, ISSN: 1063-6919
Trigeorgis G, Bousmalis K, Zafeiriou S, et al., 2014, A Deep Semi-NMF Model for Learning Hidden Representations, Publisher: IMLS
Snape PT, Zafeiriou S, 2014, Kernel-PCA Analysis of Surface Normals for Shape from Shading
Papamakarios G, Panagakis Y, Zafeiriou S, 2014, Generalised Scalable Robust principal component analysis
© 2014. The copyright of this document resides with its authors. The robust estimation of the low-dimensional subspace that spans the data from a set of high-dimensional, possibly corrupted by gross errors and outliers observations is fundamental in many computer vision problems. The state-of-the-art robust principal component analysis (PCA) methods adopt convex relaxations of ℓ0 quasi-norm-regularised rank minimisation problems. That is, the nuclear norm and the ℓ1-norm are employed. However, this convex relaxation may make the solutions deviate from the original ones. To this end, the Generalised Scalable Robust PCA (GSRPCA) is proposed, by reformulating the robust PCA problem using the Schatten p-norm and the ℓq-norm subject to orthonormality constraints, resulting in a better non-convex approximation of the original sparsity regularised rank minimisation problem. It is worth noting that the common robust PCA variants are special cases of the GSRPCA when p = q = 1 and by properly choosing the upper bound of the number of the principal components. An efficient algorithm for the GSRPCA is developed. The performance of the GSRPCA is assessed by conducting experiments on both synthetic and real data. The experimental results indicate that the GSRPCA outperforms the common state-of-the-art robust PCA methods without introducing much extra computational cost.
Elkins A, Sorros N, Zafeiriou S, et al., 2014, Do Liars Blink Differently? Automated Blink Detection during Deceptive Interviews
Valstar M, Zafeiriou S, Pantic M, 2014, Facial Action Recognition in 2D and 3D, FACE RECOGNITION IN ADVERSE CONDITIONS, Publisher: IGI GLOBAL, Pages: 167-186, ISBN: 978-1-4666-5966-7
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- Citations: 3
Zafeiriou S, Kotsia I, Pantic M, 2014, Unconstrained Face Recognition, FACE RECOGNITION IN ADVERSE CONDITIONS, Publisher: IGI GLOBAL, Pages: 16-37, ISBN: 978-1-4666-5966-7
Cheng S, Zafeiriou S, Asthana A, et al., 2014, 3D FACIAL GEOMETRIC FEATURES FOR CONSTRAINED LOCAL MODEL, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 1425-1429, ISSN: 1522-4880
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- Citations: 6
Zafeiriou L, Nikitidis S, Zafeiriou S, et al., 2014, SLOW FEATURES NONNEGATIVE MATRIX FACTORIZATION FOR TEMPORAL DATA DECOMPOSITION, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 1430-1434, ISSN: 1522-4880
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- Citations: 6
Booth J, Zafeiriou S, 2014, OPTIMAL UV SPACES FOR FACIAL MORPHABLE MODEL CONSTRUCTION, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 4672-4676, ISSN: 1522-4880
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- Citations: 22
Liwicki S, Pham M, Zafeiriou S, et al., 2014, Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
Sagonas C, Panagakis Y, Zafeiriou S, et al., 2014, RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models
Nikitidis S, Zafeiriou S, Pantic M, 2014, Merging SVMs with Linear Discriminant Analysis: A Combined Model
Pszczolkowski S, Zafeiriou S, Ledig C, et al., 2014, A Robust Similarity Measure for Nonrigid Image Registration with Outliers
Cheng S, Asthana A, Zafeiriou S, et al., 2014, Real-time generic face tracking in the wild with CUDA, Publisher: ACM, Pages: 148-151
Nicolaou MA, Zafeiriou S, Pantic M, 2014, A Unified Framework for Probabilistic Component Analysis., Publisher: Springer, Pages: 469-484
Nicolaou MA, Panagakis Y, Zafeiriou S, et al., 2014, Robust Canonical Correlation Analysis: Audio-visual Fusion for Learning Continuous Interest
Sandbach G, Zafeiriou S, Pantic M, 2013, Markov Random Field Structures for Facial Action Unit Intensity Estimation
Sagonas C, Tzimiropoulos G, Zafeiriou S, et al., 2013, 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge
Argyriou V, Kotsia I, Zafeiriou S, et al., 2013, Guest Editorial Introduction to the Special Issue on Modern Control for Computer Games, IEEE TRANSACTIONS ON CYBERNETICS, Vol: 43, Pages: 1516-1518, ISSN: 2168-2267
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- Citations: 2
Argyriou V, Zafeiriou S, Villarini B, et al., 2013, A sparse representation method for determining the optimal illumination directions in Photometric Stereo, SIGNAL PROCESSING, Vol: 93, Pages: 3027-3038, ISSN: 0165-1684
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- Citations: 5
Asthana A, Zafeiriou S, Cheng S, et al., 2013, Robust Discriminative Response Map Fitting with Constrained Local Models, 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 3444-3451, ISSN: 1063-6919
We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.
Bousmalis K, Zafeiriou S, Morency L, et al., 2013, Variational Hidden Conditional Random Fields with Coupled Dirichlet Process Mixtures
Nikolopoulos S, Zafeiriou S, Patras I, et al., 2013, High order pLSA for indexing tagged images, SIGNAL PROCESSING, Vol: 93, Pages: 2212-2228, ISSN: 0165-1684
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- Citations: 11
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.
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