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Face recognition (FR) is the preferred mode of identity recognition by humans: It is natural, robust and unintrusive. However, automatic FR techniques have failed to match up to expectations: Variations in pose, illumination and expression limit the performance of 2D FR techniques. In recent years, 3D FR has shown promise to overcome these challanges. With the availability of cheaper acquisition methods,...
In this study we have developed a real time gestural interface based on 3D dynamic hand gesture recognition using Hidden Markov Models (HMM). We developed a system, which captures and recognizes hand gestures of the user wearing colored gloves, where the hand coordinates are obtained via 3D reconstruction from stereo. The gestural interface provides supplementary features such as an interactive training...
Vein pattern is the vast network of blood vessels underneath a person's skin. These patterns in the hands are assumed to be unique to each individual and they do not change over time except in size. The properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait. In this study, we present a novel biometric technique based on the...
In this paper, we propose a methodology to generate novel motion clips from existing motion capture data. We present an automated procedure that performs clustering on the data in order to generate a robust and powerful motion graph. We discard the temporal connection between frames during clustering phase, which lets us spot hubs that motions tend to visit more often and use these poses as nodes...
STARS is a vision based real time gestural interface that allows both communicative and manipulative 3D hand gestures, which vary in motion and appearance, to control target generic personal computer applications. This input–output HMM based framework attains high recognition rates on a database consisting of 20 complex hand gestures.
Registration plays a vital role in 3D face recognition. In the registration phase, some fiducial points are needed. In this paper, a method is proposed to automatically localize landmark points. In addition, the performance of registration is affected by deformations caused by expression variations. It is asserted that regional registration and recognition is resistant to facial deformations. Finally,...
This paper presents a method to extract isolated signs from continuous sign language videos. We use sequences that approximately contain the sign that we are interested in and align the sequences to find the exact start and end frames. We compare different feature extraction methods, different alignment methods, and assess the performance of our system on a database from Turkish sign language.
Audio processing tasks, such as source separation or denoising, require the construction of realistic models that reflect physical properties of audio signals. In this paper, we modelled the variances of time-frequency coefficients of audio signals with gamma Markov random fields (GMRFs) so that the dependencies between coefficients are captured. There is positive correlation between consecutive variance...
Facial expressions play a vital role in sign language by changing the meaning of the performed sign. In this work, we propose a system composed of a facial feature tracker based on active shape models and a classifier based on hidden Markov models to recognize common facial expressions used in sign language. Face tracker works in multi-resolution and multi-view to track faces in different poses fast...
This paper contrasts two approaches to facial landmarking in 3D. The first approach is statistical in nature, and is based on modeling the shape of each feature with Gaussian mixtures. The advantage of this approach is the uniform treatment of landmarks. The second approach is a hybrid method to find the nose tip, which does not require learning, and is robust under adverse conditions. We demonstrate...
Language learning can only advance with practice and corrective feedback. The interactive system, SignTutor, evaluates users' signing and gives multimodal feedback to help improve signing.
In this work, we propose a multi-class classification strategy based on Fisher kernels. Fisher kernels combine the powers of discriminative and generative classifiers by mapping variable-length sequences to a new fixed length feature space. The mapping is based on a single generative model and the classifier is intrinsically binary. We apply a multi-class classification, instead of a binary classification,...
With increasing popularity of using motion capture hardware for motion synthesis, studies that exploit large motion databases for faster indexing and retrieval become more important. Here, an automated procedure to analyze motion databases and cluster poses according to various feature vectors is presented. We propose the use of limb centroids as an alternative feature vector. Limb centroids have...
Expressions carry vital information in sign language. In this study, we have implemented a multi-resolution active shape model (MR-ASM) tracker, which tracks 116 facial landmarks on videos. Since the expressions involve significant amount of head rotation, we employ multiple ASM models to deal with different poses. The tracked landmark points are used to extract motion features which are used by a...
Tracking a human head in a complicated scene with changing object pose, illumination conditions, and many occluding objects, is the subject of this paper. We present a general tracking algorithm, which uses a combination of object color statistics and object texture features with motion estimation. The object is defined by an ellipse window that is initially selected by the user. Color statistics...
Deformations caused by facial expression variations complicate the task of 3D face registration which is vital for successful 3D face recognition systems. In this work, we propose to use a hierarchical component-based face registration technique capable of handling the difficulties caused by non-rigid nature of faces. Local components independently registered by the iterative closest point (ICP) algorithm...
Facial expression variations and occlusions complicate the task of identifying persons from their 3D facial scans. We propose a new 3D face registration and recognition method based on local facial regions that is able to provide better accuracy in the presence of expression variations and facial occlusions. Proposed fast and flexible alignment method uses average regional models (ARMs), where local...
Automatic localization of 3D facial features is important for face recognition, tracking, modeling and expression analysis. Methods developed for 2D images were shown to have problems working across databases acquired with different illumination conditions. Expression variations, pose variations and occlusions also hamper accurate detection of landmarks. In this paper we assess a fully automatic 3D...
This paper exhibits the performance drop in 3D face recognition in the presence of expression and proposes a new 3D face registration and recognition method based on facial regions. For fast alignment, a registration method based on iterative closest point (ICP) algorithm is used which employs average regional models (ARMs). Similarity scores obtained from alignment with separate regional models are...
This paper presents a framework for audio-driven human body motion analysis and synthesis. The video is analyzed to capture the time-varying posture of the dancerpsilas body whereas the musical audio signal is processed to extract the beat information. The human body posture is extracted from multiview video information without any human intervention using a novel marker-based algorithm based on annealing...
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