4,865 research outputs found
Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking
In this paper, we propose a generative framework that unifies depth-based 3D
facial pose tracking and face model adaptation on-the-fly, in the unconstrained
scenarios with heavy occlusions and arbitrary facial expression variations.
Specifically, we introduce a statistical 3D morphable model that flexibly
describes the distribution of points on the surface of the face model, with an
efficient switchable online adaptation that gradually captures the identity of
the tracked subject and rapidly constructs a suitable face model when the
subject changes. Moreover, unlike prior art that employed ICP-based facial pose
estimation, to improve robustness to occlusions, we propose a ray visibility
constraint that regularizes the pose based on the face model's visibility with
respect to the input point cloud. Ablation studies and experimental results on
Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective
and outperforms completing state-of-the-art depth-based methods
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Models and Analysis of Vocal Emissions for Biomedical Applications
The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy
3D Face Morphing Attacks: Generation, Vulnerability and Detection
Face Recognition systems (FRS) have been found to be vulnerable to morphing
attacks, where the morphed face image is generated by blending the face images
from contributory data subjects. This work presents a novel direction for
generating face-morphing attacks in 3D. To this extent, we introduced a novel
approach based on blending 3D face point clouds corresponding to contributory
data subjects. The proposed method generates 3D face morphing by projecting the
input 3D face point clouds onto depth maps and 2D color images, followed by
image blending and wrapping operations performed independently on the color
images and depth maps. We then back-projected the 2D morphing color map and the
depth map to the point cloud using the canonical (fixed) view. Given that the
generated 3D face morphing models will result in holes owing to a single
canonical view, we have proposed a new algorithm for hole filling that will
result in a high-quality 3D face morphing model. Extensive experiments were
conducted on the newly generated 3D face dataset comprising 675 3D scans
corresponding to 41 unique data subjects and a publicly available database
(Facescape) with 100 data subjects. Experiments were performed to benchmark the
vulnerability of the {proposed 3D morph-generation scheme against} automatic
2D, 3D FRS, and human observer analysis. We also presented a quantitative
assessment of the quality of the generated 3D face-morphing models using eight
different quality metrics. Finally, we propose three different 3D face Morphing
Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face
morphing attack detection techniques.Comment: The paper is accepted at IEEE Transactions on Biometrics, Behavior
and Identity Scienc
A review on visual privacy preservation techniques for active and assisted living
This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in this taxonomy, is highlighted. These are a category of visual privacy preservation techniques, particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of privacy by design, as defined by experts in privacy and data protection law, is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091. The authors would also like to acknowledge the contribution of COST Action CA19121 - GoodBrother, Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (https://goodbrother.eu/), supported by COST (European Cooperation in Science and Technology) (https://www.cost.eu/)
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
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