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We introduce a semi-automatic tracking method that can be utilized for the analysis of facial markers in the medical condition of facial palsy. Tracking of markers will help medical physicians in evaluating this medical condition quantitatively. We use particle filtering to track markers towards measuring distances needed to evaluate the degree of facial palsy. We show that by employing tracking methods,...
Dense point clouds of naturally existing but damaged real world structures result in 3D models that may be visually unpleasant for applications such as a ‘Virtual Tour’. This paper addresses the reconstruction of such damaged regions (or, ‘holes’) in digital models. Without constraining the complexity or size of the hole, a noniterative framework based on Tensor Voting (TV) is proposed to fill-in...
Automatic age estimation is the process of using a computer to predict the age of a person automatically based on a given facial image. While this problem has numerous real-world applications, the high variability of aging patterns and the sparsity of available data present challenges for model training. Here, instead of training one global aging function, we train an individual function for each...
In this paper, we consider the kinship verification problem through face images. Genetics studies show resemblant regions on faces among immediate family members are mostly concentrated on eyes, nose, mouth, etc. This motivates us the following research. First, we construct low-level features based on the hierarchical local regions. Second, an attribute based method is proposed towards meaningful...
Iris recognition from at-a-distance face images has high applications in wide range of applications such as remote surveillance and for civilian identification. This paper presents a completely automated joint iris and periocular recognition approach from the face images acquired at-a-distance. Each of the acquired face images are used to detect and segment periocular images which are then employed...
Learning from large, multi-class data sets poses great challenges to ensemble methods. The weak learner condition makes the conventional method inappropriate to handle multi-class classification, which leads to early termination of the training process. Also, elongated training time makes learning from large data set infeasible. To circumvent these issues, we present a novel method that integrates...
Automatically identifying and analyzing head gestures is useful in many situations like smart meeting rooms and intelligent driver assistance. In this paper, we show that head movements can be broken into its elemental forms (i.e. moving and fixation states) and combinations of these elemental forms give rise to various head gestures. Our approach which we term, Optical flow based Head Movement and...
Varying lighting conditions may cause great face image appearance variance and hence seriously distort the distributions of within and between class distances, but these distributions are exactly the foundation of successful automatic recognition. In this paper, a dynamic illumination distinction description strategy is put forward to compensate for the lighting difference between probe and gallery...
The detection of salient regions from mesh surfaces is an important preprocessing step for many 3D applications, such as mesh simplification, registration, segmentation and compression, etc. The detected salient regions can facilitate the understanding of the structure and finding the regions/components that are important on 3D surfaces. This paper proposes a novel method for saliency detection by...
In this paper, a novel sparse representation based super-resolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a low resolution (LR) observation via training samples. First, a specific LR and HR over-complete dictionary pair is learned for a certain patch over the patches in all training samples with the same position. Second, K-selection mean constrain is used to...
This paper proposes an approach whereby the regional contrasts in snap-shot style portrait photographs are enhanced using pre-modern portrait paintings as aesthetic exemplars. The reference portrait painting is selected based on a comparison of the existing contrast properties of the painting and the photograph. The contrast style in the selected reference painting is transferred to the photograph...
Nonnegative Matrix Factorization (NMF), a popular compact data representation method, fails to discover the intrinsic geometrical structure of the data space. Graph regularized NMF (GrNMF) is proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data feature space. However using the original feature space directly is not appropriate because...
We propose a novel local feature descriptor, Local Gaussian Directional Pattern (LGDP), for face recognition. LGDP encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminating code than other methods. The structure of each micro-pattern is computed by using a derivative-Gaussian compass mask, and encoded by using its prominent...
One problem of existing appearance-based face recognition methods (e.g. PCA, LDA) is their weak ability of coping with local variations caused by facial expressions, motion deformation, data missing, etc. Multi-subregion fusion methods, which divide the face into a set of subregions, aim at this issue, and were reported a better performance. However, it leaves two open questions: 1. what subregions...
We propose an approach for multi-pose face tracking by association of face detection responses in two stages using multiple cues. The low-level stage uses a two-threshold strategy to merge detection responses based on location, size and pose, resulting in short but reliable tracklets. The high-level stage uses different cues for computing a joint similarity measure between tracklets. The facial cue...
We present a state-of-the-art bi-modal authentication system for mobile environments, using session variability modelling. We examine inter-session variability modelling (ISV) and joint factor analysis (JFA) for both face and speaker authentication and evaluate our system on the largest bi-modal mobile authentication database available, the MOBIO database, with over 61 hours of audio-visual data captured...
This paper proposes a new method for facial motion extraction to represent, learn and recognize observed expressions, from 4D video sequences. The approach called Deformation Vector Field (DVF) is based on Riemannian facial shape analysis and captures densely dynamic information from the entire face. The resulting temporal vector field is used to build the feature vector for expression recognition...
Face alignment is a crucial step in many facial processing applications. It has received extensive attention in the last two decades. The general approach consist in estimating the parameters of a deformable shape model, which minimize a cost function. Most of the existing methods are based on empirical cost functions. In this paper we propose to learn an ideal global cost function (i.e. the quality...
We extend the PCT (Pseudo Census Transform)-based appearance model [3] to ranking-based appearance model for face alignment. The PCT-based weak ranking function is learned using RankSVM, and the ranking appearance model (RAM) is constructed in a boosting manner. Experiments show that the PCT-based RAM is more robust and generalize better than the PCT-based boosted appearance model (BAM). The PCT-RAM...
We present a novel facial expression recognition framework using audio-visual information analysis. In particular, we design a single good image representation of the image sequence by weighted sum of registered face images where the weights are derived using auditory features. We use a still image based technique for the expression recognition task. We performed experiments using eNTERFACE'05 audio-visual...
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