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In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs(More)
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(More)
In this paper, two novel methods suitable for blind 3D mesh object watermarking applications are proposed. The first method is robust against 3D rotation, translation, and uniform scaling. The second one is robust against both geometric and mesh simplification attacks. A pseudorandom watermarking signal is cast in the 3D mesh object by deforming its(More)
The development of facial databases with an abundance of annotated facial data captured under unconstrained 'in-the-wild' conditions have made discriminative facial de-formable models the de facto choice for generic facial landmark localization. Even though very good performance for the facial landmark localization has been shown by many recently proposed(More)
Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different markups and in some cases the are problems related to the accuracy of the fiducial(More)
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(More)
The proposed Active Orientation Models (AOMs) are gen-erative 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,(More)
— 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(More)
Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why,(More)