Paper Info
Title
Real-time expression recognition from dynamic sequences of 3D facial scans
Abstract
In this paper, we address the problem of person-independent facial expression recognition in dynamic sequences of 3D face scans. To this end, an original approach is proposed that relies on automatically extracting a set of 3D facial points, and modeling their mutual distances along time. Training an Hidden Markov Model for every prototypical facial expression to be recognized, and combining them to form a multi-class classifier, an average recognition rate of 76.3% on the angry, happy and surprise expressions of the BU-4DFE database has been obtained. Comparison with competitor approaches on the same database shows that our solution is able to obtain effective results with the clear advantage of an implementation that fits to real-time constraints.
Year
DOI
Venue
2012
10.2312/3DOR/3DOR12/085-092
3DOR
Keywords
Field
DocType
average recognition rate,competitor approach,real-time expression recognition,surprise expression,prototypical facial expression,bu-4dfe database,clear advantage,facial scan,dynamic sequence,hidden markov model,person-independent facial expression recognition,facial point,solid,surface
Computer vision,Expression (mathematics),Facial expression recognition,Pattern recognition,Computer science,Facial expression,Artificial intelligence,Surprise,Hidden Markov model,Classifier (linguistics)
Conference
Citations 
PageRank 
References 
6
0.44
17
Authors
3
Name
Order
Citations
PageRank
Stefano Berretti188052.33
Alberto Del Bimbo23777420.44
Pietro Pala3123991.64