220 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
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
Valstar MF, Zafeiriou S, Pantic M, 2014, Facial action recognition in 2D and 3D, Face Recognition in Adverse Conditions, Pages: 167-186, ISBN: 9781466659667
© 2014 by IGI Global. All rights reserved. Automatic Facial Expression Analysis systems have come a long way since the earliest approaches in the early 1970s. We are now at a point where the first systems are commercially applied, most notably smile detectors included in digital cameras. As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has received significant and sustained attention within the field. Over the past 30 years, psychologists and neuroscientists have conducted extensive research on various aspects of human behaviour using facial expression analysis coded in terms of FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Mainly due to the cost effectiveness of existing recording equipment, until recently almost all work conducted in this area involves 2D imagery, despite their inherent problems relating to pose and illumination variations. In order to deal with these problems, 3D recordings are increasingly used in expression analysis research. In this chapter, the authors give an overview of 2D and 3D FACS recognition, and summarise current challenges and opportunities.
Zafeiriou S, Kotsia I, Pantic M, 2014, Unconstrained face recognition, Face Recognition in Adverse Conditions, Pages: 16-37, ISBN: 9781466659667
© 2014 by IGI Global. All rights reserved. The human face is the most well-researched object in computer vision, mainly because (1) it is a highly deformable object whose appearance changes dramatically under different poses, expressions, and, illuminations, etc., (2) the applications of face recognition are numerous and span several fields, (3) it is widely known that humans possess the ability to perform, extremely efficiently and accurately, facial analysis, especially identity recognition. Although a lot of research has been conducted in the past years, the problem of face recognition using images captured in uncontrolled environments including several illumination and/or pose variations still remains open. This is also attributed to the existence of outliers (such as partial occlusion, cosmetics, eyeglasses, etc.) or changes due to age. In this chapter, the authors provide an overview of the existing fully automatic face recognition technologies for uncontrolled scenarios. They present the existing databases and summarize the challenges that arise in such scenarios and conclude by presenting the opportunities that exist in the field.
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
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
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.
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
Elkins A, Sorros N, Zafeiriou S, et al., 2014, Do Liars Blink Differently? Automated Blink Detection during Deceptive Interviews
Pszczolkowski S, Zafeiriou S, Ledig C, et al., 2014, A Robust Similarity Measure for Nonrigid Image Registration with Outliers
Nikitidis S, Zafeiriou S, Pantic M, 2014, Merging SVMs with Linear Discriminant Analysis: A Combined Model
Sagonas C, Panagakis Y, Zafeiriou S, et al., 2014, RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models
Liwicki S, Pham M, Zafeiriou S, et al., 2014, Full-Angle Quaternions for Robustly Matching Vectors of 3D Rotations
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
Cheng S, Asthana A, Zafeiriou S, et al., 2014, Real-time generic face tracking in the wild with CUDA, Publisher: ACM, Pages: 148-151
Alabort-i-medina J, Zafeiriou S, 2014, Bayesian Active Appearance Models
Asthana A, Zafeiriou S, Cheng S, et al., 2014, Incremental Face Alignment in the Wild
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
Sagonas C, Tzimiropoulos G, Zafeiriou S, et al., 2013, 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge
Sandbach G, Zafeiriou S, Pantic M, 2013, Markov Random Field Structures for Facial Action Unit Intensity Estimation
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
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
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