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Author |
Joan Arnedo-Moreno; Agata Lapedriza |
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Title |
Visualizing key authenticity: turning your face into your public key |
Type |
Conference Article |
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Year |
2010 |
Publication |
6th China International Conference on Information Security and Cryptology |
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Pages |
605-618 |
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Abstract |
Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process. |
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Inscrypt |
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OR;MV |
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no |
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Call Number |
Admin @ si @ ArL2010c |
Serial |
2149 |
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Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
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Title |
Evaluation of Intrinsic Image Algorithms to Detect the Shadows Cast by Static Objects Outdoors |
Type |
Journal Article |
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Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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Volume |
12 |
Issue |
10 |
Pages |
13333-13348 |
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Abstract |
In some automatic scene analysis applications, the presence of shadows becomes a nuisance that is necessary to deal with. As a consequence, a preliminary stage in many computer vision algorithms is to attenuate their effect. In this paper, we focus our attention on the detection of shadows cast by static objects outdoors, as the scene is viewed for extended periods of time (days, weeks) from a fixed camera and considering daylight intervals where the main source of light is the sun. In this context, we report two contributions. First, we introduce the use of synthetic images for which ground truth can be generated automatically, avoiding the tedious effort of manual annotation. Secondly, we report a novel application of the intrinsic image concept to the automatic detection of shadows cast by static objects in outdoors. We make both a quantitative and a qualitative evaluation of several algorithms based on this image representation. For the quantitative evaluation, we used the synthetic data set, while for the qualitative evaluation we used both data sets. Our experimental results show that the evaluated methods can partially solve the problem of shadow detection. |
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OR;MV |
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no |
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Admin @ si @ ISR2012b |
Serial |
2173 |
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Author |
Fadi Dornaika; A.Assoum; Bogdan Raducanu |
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Title |
Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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Volume |
7626 |
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Pages |
575-583 |
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Abstract |
A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis. |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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OR;MV |
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no |
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Call Number |
Admin @ si @ DAR2012 |
Serial |
2174 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Out-of-Sample Embedding by Sparse Representation |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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Volume |
7626 |
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Pages |
336-344 |
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Abstract |
A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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OR;MV |
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no |
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Call Number |
Admin @ si @ RaD2012c |
Serial |
2175 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Pose-Invariant Face Recognition in Videos for Human-Machine Interaction |
Type |
Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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Volume |
7584 |
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Pages |
566.575 |
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Abstract |
Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-33867-0 |
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ECCVW |
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OR;MV |
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no |
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Call Number |
Admin @ si @ RaD2012e |
Serial |
2182 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Title |
Analysis and Recognition of Facial Expressions in Videos Using Facial Shape Deformation |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Facial Expressions: Dynamic Patterns, Impairments and Social Perceptions |
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Pages |
157-178 |
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NOVA Publishers |
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Editor |
S.E. Carter |
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OR;MV |
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no |
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Call Number |
Admin @ si @ DoR2012 |
Serial |
2183 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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Journal |
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Year |
2013 |
Publication |
Komputer Sapiens |
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KS |
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Volume |
1 |
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Pages |
20-25 |
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OR;MV |
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no |
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Admin @ si @ TSR2013 |
Serial |
2231 |
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Author |
David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson |
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Title |
Automated Prediction of Preferences Using Facial Expressions |
Type |
Journal Article |
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Year |
2014 |
Publication |
PloS one |
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Plos |
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Volume |
9 |
Issue |
2 |
Pages |
e87434 |
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Abstract |
We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. |
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OR;MV |
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no |
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Call Number |
Admin @ si @ MNT2014 |
Serial |
2453 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Robust Head Gestures Recognition for Assistive Technology |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Pattern Recognition |
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Volume |
8495 |
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Pages |
152-161 |
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This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture. |
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Springer International Publishing |
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0302-9743 |
ISBN |
978-3-319-07490-0 |
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OR;MV |
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no |
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Admin @ si @ TSR2014b |
Serial |
2505 |
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Author |
Manuel Graña; Bogdan Raducanu |
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Title |
Special Issue on Bioinspired and knowledge based techniques and applications |
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Journal Article |
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Year |
2015 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
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1-3 |
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OR;MV |
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no |
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Admin @ si @ GrR2015 |
Serial |
2598 |
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Author |
Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika |
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Title |
Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics |
Type |
Conference Article |
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Year |
2014 |
Publication |
1st Workshop on Computer Vision for Affective Computing |
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Pages |
1-8 |
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Abstract |
Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression. |
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Address |
Singapore; November 2014 |
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ACCV |
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OR;MV |
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no |
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Call Number |
Admin @ si @ RBD2014 |
Serial |
2599 |
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Permanent link to this record |
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Author |
R. Clariso; David Masip; A. Rius |
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Title |
Student projects empowering mobile learning in higher education |
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Journal |
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2014 |
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Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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11 |
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192-207 |
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1698-580X |
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OR;MV |
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no |
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Admin @ si @ CMR2014 |
Serial |
2619 |
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Author |
B. Zhou; Agata Lapedriza; J. Xiao; A. Torralba; A. Oliva |
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Title |
Learning Deep Features for Scene Recognition using Places Database |
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Conference Article |
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Year |
2014 |
Publication |
28th Annual Conference on Neural Information Processing Systems |
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487-495 |
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Montreal; Canada; December 2014 |
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NIPS |
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OR;MV |
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no |
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Admin @ si @ ZLX2014 |
Serial |
2621 |
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Author |
Agata Lapedriza; David Masip; D.Sanchez |
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Title |
Emotions Classification using Facial Action Units Recognition |
Type |
Conference Article |
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Year |
2014 |
Publication |
17th International Conference of the Catalan Association for Artificial Intelligence |
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269 |
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55-64 |
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In this work we build a system for automatic emotion classification from image sequences. We analyze subtle changes in facial expressions by detecting a subset of 12 representative facial action units (AUs). Then, we classify emotions based on the output of these AUs classifiers, i.e. the presence/absence of AUs. We base the AUs classification upon a set of spatio-temporal geometric and appearance features for facial representation, fusing them within the emotion classifier. A decision tree is trained for emotion classifying, making the resulting model easy to interpret by capturing the combination of AUs activation that lead to a particular emotion. For Cohn-Kanade database, the proposed system classifies 7 emotions with a mean accuracy of near 90%, attaining a similar recognition accuracy in comparison with non-interpretable models that are not based in AUs detection. |
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978-1-61499-451-0 |
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CCIA |
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OR;MV |
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no |
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Call Number |
Admin @ si @ LMS2014 |
Serial |
2622 |
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Permanent link to this record |
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Author |
Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh |
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Title |
Facial expression recognition based on multi observations with application to social robotics |
Type |
Book Chapter |
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Year |
2015 |
Publication |
Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance |
Abbreviated Journal |
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Pages |
153-166 |
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Abstract |
Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication
which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression. |
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Bruce Flores |
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Admin @ si @ DRB2015 |
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2720 |
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