NDLI: Probabilistic combination of multiple modalities to detect interest

Content Provider	IEEE Xplore Digital Library
Author	Kapoor, A. Picard, R.W. Ivanov, Y.
Copyright Year	2004
Description	Author affiliation: Massachusetts Inst. of Techonol., Cambridge, MA, USA (Kapoor, A.; Picard, R.W.)
Abstract	This paper describes a new approach to combine multiple modalities and applies it to the problem of affect recognition. The problem is posed as a combination of classifiers in a probabilistic framework that naturally explains the concepts of experts and critics. Each channel of data has an expert associated that generates the beliefs about the correct class. Probabilistic models of error and the critics, which predict the performance of the expert on the current input, are used to combine the expert's beliefs about the correct class. The method is applied to detect the affective state of interest using information from the face, postures and task the subjects are performing. The classification using multiple modalities achieves a recognition accuracy of 67.8%, outperforming the classification using individual modalities. Further, the proposed combination scheme achieves the greatest reduction in error when compared with other classifier combination methods.
Starting Page	969
Ending Page	972
File Size	290503
Page Count	4
File Format	PDF
ISBN	0769521282
ISSN	10514651
DOI	10.1109/ICPR.2004.1334690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Training data Predictive models Error correction Face recognition Boosting Bagging Fusion power generation Stacking Feature extraction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Boosting in Random Subspaces for Face Recognition

Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation

Bagging.LMS: A Bagging-based Linear Fusion with Least-Mean-Square Error Update for Regression

Ranking Model for Facial Age Estimation

Informational classifier fusion

Data Fusion and Confidence Measure in Image Feature Detection

Linear sparse feature based face detection in gray images

Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm

Learning SURF Cascade for Fast and Accurate Object Detection

Probabilistic combination of multiple modalities to detect interest

Similar Documents

Boosting in Random Subspaces for Face Recognition

Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation

Bagging.LMS: A Bagging-based Linear Fusion with Least-Mean-Square Error Update for Regression

Ranking Model for Facial Age Estimation

Informational classifier fusion

Data Fusion and Confidence Measure in Image Feature Detection

Linear sparse feature based face detection in gray images

Off-line character recognition using HMM by multiple directional feature extraction and voting with bagging algorithm

Learning SURF Cascade for Fast and Accurate Object Detection

Probabilistic combination of multiple modalities to detect interest