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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...
In this paper, we present a graph based face representation for efficient age invariant face recognition. The graph contains information on the appearance and geometry of facial feature points. An age model is learned for each individual and a graph space is built using the set of feature descriptors extracted from each face image. A two-stage method for matching is developed, where the first stage...
In this paper we present a biometric approach, based on lip shape. We have performed an image preprocessing, in order to detect the face of a person image. After this, we have enhanced the lips image using a color transformation, and next we do its detection. The parameterization is based on lips contour points. Those points have been transformed by a Hidden Markov Model (HMM) kernel, using a minimization...
This paper summarizes results of face association experiments on real low resolution data from airport and the Labeled faces in the Wild (LFW) database. The objective of experiments is to evaluate different face alignment methods and their contribution to face association as such. The first alignment method used is Sequential Learnable Linear Predictor (SLLiP), originally developed for object tracking...
This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The facial action coding system (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping action units (Aus). AUs have intensities ranging from absent to maximal on...
Active appearance models are widely used to match statistical models of shape and appearance to new images rapidly. They work by finding model parameters which minimise the sum of squares of residual differences between model and target image. A limitation of AAMs is that they are not robust to a large set of gross outliers. Using a robust kernel can help, but there are potential problems in determining...
This paper proposes a learning based framework for efficient 3D face reconstruction. We transfer the 3D reconstruction into a statistical learning problem of finding appropriate mapping between texture and depth subspaces. Instead of using grayscales to directly estimate the depth, we use local binary pattern (LBP) to further encode the face texture, providing robustness for depth estimation under...
In recent years, 3D face recognition has obtained much attention. Using 2D face image as probe and 3D face data as gallery is an alternative method to deal with computation complexity, expensive equipment and fussy pretreatment in 3D face recognition systems. In this paper we propose a learning based 2D-3D face matching method using the CCA to learn the mapping between 2D face image and 3D face data...
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