1,720,969 research outputs found
Accurate 3D model based face tracking for facial expression recognition
No full textThe recovery of the 3-D movement of the face is an important operation for many applications like human machine interaction, video surveillance, MPEG-4 compression etc.
This paper presents a method to obtain from a video input, a normalized face in a frontal pose, by recovering the fullmotion
of the head using 3D head model. From some characteristic face points given on the first frame, an approximated 3D model of the face is reconstructed. Using this model, the full motion of the head is computed automatically.
Evidently, in order to compensate errors due to the rough 3D model, a combination of several techniques has been used to reach a strong robustness. The algorithm has
been tested on synthetic videos and it has been compared with a standard multi-camera system for the 3D tracking (Elite 2002 System). The results in both cases are good.
The proposed approach is part of a facial expression analysis system. Our aim is to detect the facial expression in situations characterized by a moderate head motion. For
this reason head motion recovering is fundamental. Once recovered the pose, we are able to obtain frontal normalized facial image that makes expression analysis easier
Upper-face expression features extraction system for video sequences
No full textNowadays there are not any reliable systems able to codify a video streaming according to the FACS. The few system that are able to do it works on frontal, or however not natural postures. The system we propose, takes advantage from a robust 3D tracking based on a 3D template, in order to
extract some features correlated to the facial expressions.
Moreover, this system provides for each feature a reliability level according to the presence of some occlusions, or due to some errors in tracking that could hide or make the reconstruction on the template, become critical
Facial identification problem : a tracking based approach
Full text linkThis paper presents a method for face identification using a query by example approach. Our technique is suitable for use within Ambient Security Environments and is
robust across variations in pose, expression and illuminations conditions. To account for these variations, we use a face template matching algorithm based on a 3D head
model created from a single frontal face image. Thanks to our tracking-based approach our algorithm is able to extract
simultaneously all parameters related to the face expression and to the 3D posture. With these estimates, we are able to reconstruct a frontal, neutral and normalized image on which dissimilarity analysis for identification and anomalies detection is performed. Our tracking process combined with dissimilarity analysis was tested on Kanade-Cohn database for expression independent identification and several other experimental databases for robustness
Tracking based face identification : away to manage occlusions, and illumination, posture and expression changes
No full textThis paper presents a method for face identification using an eigenfaces approach. Our technique is suitable for use within Ambient Security Environments and is
robust across variations in pose, expression and illuminations conditions. To account for these variations, we use a face template matching algorithm based on a 3D head model. Thanks to our tracking-based approach our algorithm is able to extract simultaneously
all parameters related to the face expression and to the 3D posture. With these estimates, we are able to reconstruct a frontal, neutral and normalized image on
which an eigenface classification for identification is performed
Face tracking algorithm robust to pose, illumination and face expression changes : a 3D parametric model approach
No full textConsidering the face as an object that moves through a scene, the posture related to the camera’s point of view and the texture both may change the aspect of the object considerably. These changes are tightly coupled with the alterations in illumination conditions when the subject moves or even when some modifications happen in illumination conditions (light switched on or off etc.). This paper presents a method for tracking a face on a video sequence by recovering the full-motion and the expression deformations of the head using 3D
expressive head model. Taking advantage from a 3D triangle based face model, we are able to deal with any kind of illumination changes and face expression movements.
In this parametric model, any changes can be
defined as a linear combination of a set of weighted basis that could easily be included in a minimization algorithm using a classical Newton optimization approach. The 3D model of the face is created using some
characteristic face points given on the first frame. Using a gradient descent approach, the algorithm is able to extract simultaneously the parameters related to the face expression, the 3D posture and the virtual illumination conditions. The algorithm has been tested on Kanade-Cohn database (Kanade et al., 2000) for
expression estimation and its precision has been compared with a standard multi-camera system for the 3D tracking. (G. Ferrigno and A. Pedotti, 1985). Regarding illumination tests, we use synthetic movie created using standard 3D-mesh animation tools and real experimental videos created in very extreme
illumination condition. The results in all the cases are promising even with great head movements and changes in the expression and the illumination conditions. The proposed approach has a twofold application as a
part of a facial expression analysis system and preprocessing for identification systems (expression, pose and illumination normalization)
3D Expressive face model-based tracking algorithm
No full textThis paper presents a method for tracking a face on a video sequence, by recovering the full-motion and the expres sion deformation of the face using 3D expressive facial model. From some characteristic face points given on the first frame, an approximated 3D model of the face is re constructed. Using a steepest descent image approach, the algorithm is able to extract simultaneously the parameters related to the face expression and to the 3D posture. The algorithm has been tested on the Kanade-Cohn database [1] and its precision has been compared with a standard multi camera system for the 3D tracking (ELITE2002 System). The results in both cases are good. The proposed approach is part of a facial expression analysis system. Our aim is to detect the facial expressions in situations characterized by a moderate head motion in realistic experimental conditions (illumination from the ceiling, and subjects not in frontal pose)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A portable electroencephalogram acquisition system dedicated to the Brain Computer Interface
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