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Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as FRGC, less effort was spent on studying the robustness of algorithms to quality degradations. In this paper, we present a study of the robustness of four state of the art algorithms...
We present a Gaussian Mixture Model (GMM) fitting method for estimating the unknown neutral face shape for frontal facial expression recognition using geometrical features. Subtracting the estimated neutral face, which is related to the identity-specific component of the shape leaves us with the component related to the variations resulting from facial expressions. Experimental results on the Extended...
3D shape data for face recognition is advantageous to its 2D counterpart for being invariant to illumination and pose. However, expression variations and occlusions still remain as major challenges since the shape distortions hinder accurate matching. Numerous algorithms developed to overcome this problem mainly propose region-based approaches, where similarity scores are calculated separately by...
The 3D face recognition literature has many papers that represent facial shapes as collections of curves of different kinds (level-curves, iso-level curves, radial curves, profiles, geodesic polarization, iso-depth lines, iso-stripes, etc.). In contrast with the holistic approaches, the approaches that match faces based on whole surfaces, the curve-based parametrization allows local analysis of facial...
This paper addresses the problem of identifying a subject from a caricature. A caricature is a facial sketch of a subject's face that exaggerates identifiable facial features beyond realism, while still conveying his identity. To enable this task, we propose a set of qualitative facial features that encodes the appearance of both caricatures and photographs. We utilized crowdsourcing, through Amazon's...
In this paper, we present a method to face recognition which considers local shape information, weight of interesting region and texture information by Gabor filter, high-pass filter and local binary patterns, respectively. The face area can be largely divided into two dominant parts that one has high frequency domain and the other has low frequency domain. High frequency parts are interesting region...
We present a method to estimate, based on the horizontal symmetry, an intrinsic coordinate system of faces scanned in 3D. We show that this coordinate system provides an excellent basis for subsequent landmark positioning and model-based refinement such as Active Shape Models, outperforming other -explicit- landmark localisation methods including the commonly-used ICP+ASM approach.
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Most current methods treat them as independent problems, hence ignore the interactions between facial feature points and facial actions. In this paper, we introduce a probabilistic framework based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent...
While identical twins identification is a well known challenge in face recognition, it seems that no work has explored automatic ear recognition for identical twin identification. Ear image recognition has been studied for years, but Iannarelli (1989) appears to be the only work mentioning the twin identification (performed manually). We here explore the possibility of automatic twin identification...
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...
We propose an acquisition and recognition system based only on periocular biometric using the COTS PTZ camera to tackle the difficulty that the full face recognition approach has encountered in highly unconstrained real-world scenario, especially for capturing and recognizing uncooperative and non-cooperative subjects with expression, closed eyes, and facial occlusions. We evaluate our algorithm on...
This paper proposes a novel use of 2D barcodes to store biometric data, in particular for facial recognition, which represents one of the least invasive biometric techniques. To accomplish this, we deploy 2D color barcodes, which allow larger storage capabilities than traditional 2D barcodes. To improve the quality of facial recognition, we combine local feature descriptors, such as SURF descriptors,...
We propose an approach to employ eigen light-fields for face recognition across pose on video. Faces of a subject are collected from video frames and combined based on the pose to obtain a set of probe light-fields. These probe data are then projected to the principal subspace of the eigen light-fields within which the classification takes place. We modify the original light-field projection and found...
The complexity of human face expressions makes its information duplication difficult. A studied approach that combines different 3D facial recognition algorithms could overcome the weak information that is recovered from each algorithm individually. In this paper, it is shown that 3D face recognition subjected to facial expression is improved by combining three different algorithms. The results generated...
Conditional Random Fields (CRFs) can be used as a discriminative approach for simultaneous sequence segmentation and frame labeling. Latent-Dynamic Conditional Random Fields (LDCRFs) incorporates hidden state variables within CRFs which model sub-structure motion patterns and dynamics between labels. Motivated by the success of LDCRFs in gesture recognition, we propose a framework for automatic facial...
Art metrics, a field of study that identifies, classifies and authenticates virtual reality avatars and intelligent software agents, has been proposed as a tool for fighting crimes taking place in virtual reality communities and in multiplayer game worlds. Forensic investigators are interested in developing tools for accurate and automated tracking and recognition of avatar faces. In this paper, we...
A 3D face recognition method is proposed. For feature extraction, 3D point set is divided into number of slices to be projected on the central planes parallel to the slices. Feature vectors are formed by approximating best fitting polynomials for data on the planes. A multimodal approach in the score level and corresponding distance metrics for both unimodal and multimodal cases are presented.
Pose variance is one of the most challenging problem to 2D face recognition. In this paper, a novel frontal view face synthesizing strategy is introduced to improve the performance of traditional face recognition methods on non-frontal view input images. Given several non-frontal input faces, our minimum bending synthesizing strategy automatically picks up and merges information, to realize most natural...
This paper presents a multi-modal affect recognition system that is capable of effectively estimating human affective states through analyzing and fusing a number of non-invasive external cues. The proposed system consists of a probabilistic information fusion model based on the influence diagram and a set of data analysis, feature extraction and affect recognition modules for processing heterogeneous...
This paper presents a mesh-based approach for 3D face recognition using a novel local shape descriptor and a SIFT-like matching process. Both maximum and minimum curvatures estimated in the 3D Gaussian scale space are employed to detect salient points. To comprehensively characterize 3D facial surfaces and their variations, we calculate weighted statistical distributions of multiple order surface...
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