166 results found
Alabort-i-Medina J, Zafeiriou S, 2017, A Unified Framework for Compositional Fitting of Active Appearance Models, INTERNATIONAL JOURNAL OF COMPUTER VISION, Vol: 121, Pages: 26-64, ISSN: 0920-5691
© 2017 The Author(s) We present large scale facial model (LSFM)—a 3D Morphable Model (3DMM) automatically constructed from 9663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline, informed by an evaluation of state-of-the-art dense correspondence techniques. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM model but also models tailored for specific age, gender or ethnicity groups. We utilize the proposed model to perform age classification from 3D shape alone and to reconstruct noisy out-of-sample data in the low-dimensional model space. Furthermore, we perform a systematic analysis of the constructed 3DMM models that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline, as well as the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity.
Cheng S, Marras I, Zafeiriou S, et al., 2017, Statistical non-rigid ICP algorithm and its application to 3D face alignment, IMAGE AND VISION COMPUTING, Vol: 58, Pages: 3-12, ISSN: 0262-8856
Chrysos GG, Antonakos E, Snape P, et al., 2017, A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”, International Journal of Computer Vision, Pages: 1-35, ISSN: 0920-5691
© 2017 The Author(s) Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as “in-the-wild”). This is partially attributed to the fact that comprehensive “in-the-wild” benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking “in-the-wild”. Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.
Chrysos GG, Zafeiriou S, 2017, Deep Face Deblurring, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Vol: 2017-July, Pages: 2015-2024, ISSN: 2160-7508
© 2017 IEEE. Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention. In this work, we develop such a domain-specific method to tackle deblurring of human faces, henceforth referred to as face deblurring. Studying faces is of tremendous significance in computer vision, however face deblurring has yet to demonstrate some convincing results. This can be partly attributed to the combination of i) poor texture and ii) highly structure shape that yield the contour/gradient priors (that are typically used) sub-optimal. In our work instead of making assumptions over the prior, we adopt a learning approach by inserting weak supervision that exploits the well-documented structure of the face. Namely, we utilise a deep network to perform the deblurring and employ a face alignment technique to pre-process each face. We additionally surpass the requirement of the deep network for thousands training samples, by introducing an efficient framework that allows the generation of a large dataset. We utilised this framework to create 2MF2, a dataset of over two million frames. We conducted experiments with real world blurred facial images and report that our method returns a result close to the sharp natural latent image.
Georgakis C, Panagakis Y, Zafeiriou S, et al., 2017, The Conflict Escalation Resolution (CONFER) Database, IMAGE AND VISION COMPUTING, Vol: 65, Pages: 37-48, ISSN: 0262-8856
Sagonas C, Panagakis Y, Leidinger A, et al., 2017, Robust Joint and Individual Variance Explained, 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 5739-5748, ISSN: 1063-6919
Sagonas C, Panagakis Y, Zafeiriou S, et al., 2017, Robust Statistical Frontalization of Human and Animal Faces, INTERNATIONAL JOURNAL OF COMPUTER VISION, Vol: 122, Pages: 270-291, ISSN: 0920-5691
Sagonas C, Ververas E, Panagakis Y, et al., 2017, Recovering Joint and Individual Components in Facial Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828
IEEE A set of images depicting faces with different expressions or in various ages consists of components that are shared across all images (i.e., joint components) and imparts to the depicted object the properties of human faces and individual components that are related to different expressions or age groups. Discovering the common (joint) and individual components in facial images is crucial for applications such as facial expression transfer. The problem is rather challenging when dealing with images captured in unconstrained conditions and thus are possibly contaminated by sparse non-Gaussian errors of large magnitude (i.e., sparse gross errors) and contain missing data. In this paper, we investigate the use of a method recently introduced in statistics, the so-called Joint and Individual Variance Explained (JIVE) method, for the robust recovery of joint and individual components in visual facial data consisting of an arbitrary number of views. Since, the JIVE is not robust to sparse gross errors, we propose alternatives, which are 1) robust to sparse gross, non-Gaussian noise, 2) able to automatically find the individual components rank, and 3) can handle missing data. We demonstrate the effectiveness of the proposed methods to several computer vision applications, namely facial expression synthesis and 2D and 3D face age progression in-the-wild.
Trigeorgis G, Bousmalis K, Zafeiriou S, et al., 2017, A Deep Matrix Factorization Method for Learning Attribute Representations, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 39, Pages: 417-429, ISSN: 0162-8828
Trigeorgis G, Nicolaou M, Zafeiriou S, et al., 2017, Deep Canonical Time Warping for simultaneous alignment and representation learning of sequences., IEEE Trans Pattern Anal Mach Intell
Machine learning algorithms for the analysis of time-series often depend on the assumption that utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards temporal alignment are either applied directly on the observation space or simply utilise linear projections - thus failing to capture complex, hierarchical non-linear representations that may prove beneficial, especially when dealing with multi-modal data (e.g., visual and acoustic information). To this end, we present Deep Canonical Time Warping (DCTW), a method that automatically learns non-linear representations of multiple time-series that are (i) maximally correlated in a shared subspace, and (ii) temporally aligned. Furthermore, we extend DCTW to a supervised setting, where during training, available labels can be utilised towards enhancing the alignment process. By means of experiments on four datasets, we show that the representations learnt significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with heterogeneous feature sets, such as the temporal alignment of acoustic and visual information.
Trigeorgis G, Snape P, Kokkinos I, et al., 2017, Face Normals "in-the- wild" using Fully Convolutional Networks, 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 340-349, ISSN: 1063-6919
Wang M, Panagakis Y, Snape P, et al., 2017, Learning the Multilinear Structure of Visual Data, 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 6053-6061, ISSN: 1063-6919
Zafeiriou S, Trigeorgis G, Chrysos G, et al., 2017, The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution, Pages: 2116-2125, ISSN: 2160-7508
© 2017 IEEE. In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup of facial landmarks). Furthermore, we increase considerably the number of annotated images so that deep learning algorithms can be robustly applied to the problem. The results of the Menpo challenge demonstrate that recent deep learning architectures when trained with the abundance of data lead to excellent results. Finally, we discuss directions for future benchmarks in the topic.
Antonakos E, Snape P, Trigeorgis G, et al., 2016, ADAPTIVE CASCADED REGRESSION, 23rd IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 1649-1653, ISSN: 1522-4880
Booth J, Roussos A, Zafeiriou S, et al., 2016, A 3D Morphable Model learnt from 10,000 faces, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 5543-5552, ISSN: 1063-6919
Gligorijevic V, Panagakis Y, Zafeiriou S, 2016, Fusion and Community Detection in Multi-layer Graphs, 23rd International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 1327-1332, ISSN: 1051-4651
Hong X, Zhao G, Zafeiriou S, et al., 2016, Capturing correlations of local features for image representation, NEUROCOMPUTING, Vol: 184, Pages: 99-106, ISSN: 0925-2312
Hovhannisyan V, Parpas P, Zafeiriou S, 2016, MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization, SIAM JOURNAL ON IMAGING SCIENCES, Vol: 9, Pages: 1829-1857, ISSN: 1936-4954
Kampouris C, Zafeiriou S, Ghosh A, et al., 2016, Fine-Grained Material Classification Using Micro-geometry and Reflectance, 14th European Conference on Computer Vision (ECCV), Publisher: SPRINGER INT PUBLISHING AG, Pages: 778-792, ISSN: 0302-9743
Panagakis Y, Nicolaou MA, Zafeiriou S, et al., 2016, Robust Correlated and Individual Component Analysis, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 38, Pages: 1665-1678, ISSN: 0162-8828
Sagonas C, Antonakos E, Tzimiropoulos G, et al., 2016, 300 Faces In-The-Wild Challenge: database and results, IMAGE AND VISION COMPUTING, Vol: 47, Pages: 3-18, ISSN: 0262-8856
Sagonas C, Panagakis Y, Arunkumar S, et al., 2016, Back to the Future: A Fully Automatic Method for Robust Age Progression, 23rd International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 4226-4231, ISSN: 1051-4651
Snape P, Pszczolkowski S, Zafeiriou S, et al., 2016, A robust similarity measure for volumetric image registration with outliers, IMAGE AND VISION COMPUTING, Vol: 52, Pages: 97-113, ISSN: 0262-8856
Trigeorgis G, Nicolaou MA, Schuller BW, et al., 2016, Deep Canonical Time Warping, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 5110-5118, ISSN: 1063-6919
Trigeorgis G, Ringeval F, Brueckner R, et al., 2016, ADIEU FEATURES? END-TO-END SPEECH EMOTION RECOGNITION USING A DEEP CONVOLUTIONAL RECURRENT NETWORK, IEEE International Conference on Acoustics, Speech, and Signal Processing, Publisher: IEEE, Pages: 5200-5204, ISSN: 1520-6149
Trigeorgis G, Snape P, Nicolaou MA, et al., 2016, Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 4177-4187, ISSN: 1063-6919
Zafeiriou L, Antonakos E, Zafeiriou S, et al., 2016, Joint Unsupervised Deformable Spatio-Temporal Alignment of Sequences, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Publisher: IEEE, Pages: 3382-3390, ISSN: 1063-6919
Zafeiriou L, Nicolaou MA, Zafeiriou S, et al., 2016, Probabilistic Slow Features for Behavior Analysis, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Vol: 27, Pages: 1034-1048, ISSN: 2162-237X
Zafeiriou S, Papaioannou A, Kotsia I, et al., 2016, Facial Affect "in-the-wild": A survey and a new database, 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Publisher: IEEE, Pages: 1487-1498, ISSN: 2160-7508
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