# STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment of Computing

Professor in Machine Learning & Computer Vision

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### Contact

+44 (0)20 7594 8461s.zafeiriou CV

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### Location

375Huxley BuildingSouth Kensington Campus

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## Publications

Publication Type
Year
to

221 results found

Zafeiriou S, Tefas A, Pitas I, 2007, Learning Discriminant Person-Specific Facial Models Using Expandable Graphs, IEEE Transactions on Information Forensics and Security, Vol: 2, Pages: 55-68, ISSN: 1556-6013

Journal article

Goudelis G, Zafeiriou S, Tefas A, Pitas Iet al., 2007, A Novel Kernel Discriminant Analysis for Face Verification., Publisher: IEEE, Pages: 493-496

Conference paper

Zafeiriou S, Tefas A, Pitas I, 2007, Discriminant Graph Structures for Face Verification., Publisher: IEEE, Pages: 497-500

Conference paper

Nikolopoulos S, Zafeiriou S, Sidiropoulos P, Nikolaidis N, Pitas Iet al., 2006, Image Replica Detection using R-Trees and Linear Discriminant Analysis, 2006 IEEE International Conference on Multimedia and Expo, Publisher: IEEE

Conference paper

Zafeiriou S, Tefas A, Buciu I, Pitas Iet al., 2006, Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification, IEEE Transactions on Neural Networks, Vol: 17, Pages: 683-695, ISSN: 1045-9227

Journal article

Zafeiriou S, Tefas A, Pitas I, 2005, Blind Robust Watermarking Schemes for Copyright Protection of 3D Mesh Objects, IEEE Transactions on Visualization and Computer Graphics, Vol: 11, Pages: 596-607, ISSN: 1077-2626

Journal article

Zafeiriou S, Tefas A, Pitas I, 2005, Exploiting discriminant information in elastic graph matching., Publisher: IEEE, Pages: 768-771

Conference paper

Zafeiriou S, Tefas A, Buciu I, Pitas Iet al., 2005, Class-Specific Discriminant Non-negative Matrix Factorization for Frontal Face Verification., Publisher: Springer, Pages: 206-215

Conference paper

Zafeiriou S, Tefas A, Pitas I, 2004, A blind robust watermarking scheme for copyright protection of 3d mesh models., Publisher: IEEE, Pages: 1569-1572

Conference paper

Chrysos GG, Panagakis Y, Zafeiriou S, Visual Data Augmentation through Learning

The rapid progress in machine learning methods has been empowered by i) hugedatasets that have been collected and annotated, ii) improved engineering (e.g.data pre-processing/normalization). The existing datasets typically includeseveral million samples, which constitutes their extension a colossal task. Inaddition, the state-of-the-art data-driven methods demand a vast amount ofdata, hence a standard engineering trick employed is artificial dataaugmentation for instance by adding into the data cropped and (affinely)transformed images. However, this approach does not correspond to any change inthe natural 3D scene. We propose instead to perform data augmentation through learning realisticlocal transformations. We learn a forward and an inverse transformation thatmaps an image from the high-dimensional space of pixel intensities to a latentspace which varies (approximately) linearly with the latent space of arealistically transformed version of the image. Such transformed images can beconsidered two successive frames in a video. Next, we utilize thesetransformations to learn a linear model that modifies the latent spaces andthen use the inverse transformation to synthesize a new image. We argue thatthe this procedure produces powerful invariant representations. We perform bothqualitative and quantitative experiments that demonstrate our proposed methodcreates new realistic images.

Journal article

Sagonas C, Panagakis Y, Zafeiriou S, Pantic Met al., Face frontalization for Alignment and Recognition

Recently, it was shown that excellent results can be achieved in both facelandmark localization and pose-invariant face recognition. These breakthroughsare attributed to the efforts of the community to manually annotate facialimages in many different poses and to collect 3D faces data. In this paper, wepropose a novel method for joint face landmark localization and frontal facereconstruction (pose correction) using a small set of frontal images only. Byobserving that the frontal facial image is the one with the minimum rank fromall different poses we formulate an appropriate model which is able to jointlyrecover the facial landmarks as well as the frontalized version of the face. Tothis end, a suitable optimization problem, involving the minimization of thenuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method isassessed in frontal face reconstruction (pose correction), face landmarklocalization, and pose-invariant face recognition and verification byconducting experiments on $6$ facial images databases. The experimental resultsdemonstrate the effectiveness of the proposed method.

Journal article

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