| |
Records |
Links |
|
Author |
Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
|
| |
Title |
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
28 |
Issue |
8 |
Pages  |
1548-1568 |
|
| |
Keywords |
Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
|
| |
Abstract |
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ COC2016 |
Serial |
2718 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xavier Otazu; Oriol Pujol |
|
| |
Title |
Wavelet based approach to cluster analysis. Application on low dimensional data sets |
Type |
Journal Article |
| |
Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
27 |
Issue |
14 |
Pages |
1590–1605 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; CIC; HuPBA |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ OtP2006 |
Serial |
658 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu |
|
| |
Title |
Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
12 |
Issue |
2 |
Pages |
1702-1719 |
|
| |
Keywords |
|
|
| |
Abstract |
IF=1.77 (2010)
Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog.
The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Molecular Diversity Preservation International |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ EBV2012 |
Serial |
1885 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera |
|
| |
Title |
Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology |
Abbreviated Journal |
TCSVT |
|
| |
Volume |
29 |
Issue |
6 |
Pages |
1729-1740 |
|
| |
Keywords |
Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding |
|
| |
Abstract |
Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. |
|
| |
Address |
June 2019, |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ AAK2018 |
Serial |
3213 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Ana Puig; Oscar Amoros; Maria Salamo |
|
| |
Title |
Intelligent GPGPU Classification in Volume Visualization: a framework based on Error-Correcting Output Codes |
Type |
Journal Article |
| |
Year |
2011 |
Publication |
Computer Graphics Forum |
Abbreviated Journal |
CGF |
|
| |
Volume |
30 |
Issue |
7 |
Pages |
2107-2115 |
|
| |
Keywords |
|
|
| |
Abstract |
IF JCR 1.455 2010 25/99
In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this paper, we present a framework of intelligent methods to label on-demand multiple regions of interest. These methods can be split into a two-level GPU-based labelling algorithm that computes in time of rendering a set of labelled structures using the Machine Learning Error-Correcting Output Codes (ECOC) framework. In a pre-processing step, ECOC trains a set of Adaboost binary classifiers from a reduced pre-labelled data set. Then, at the testing stage, each classifier is independently applied on the features of a set of unlabelled samples and combined to perform multi-class labelling. We also propose an alternative representation of these classifiers that allows to highly parallelize the testing stage. To exploit that parallelism we implemented the testing stage in GPU-OpenCL. The empirical results on different data sets for several volume structures shows high computational performance and classification accuracy. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; HuPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ EPA2011 |
Serial |
1881 |
|
| Permanent link to this record |
