Paper Info
Title
Head-Pose Invariant Facial Expression Recognition Using Convolutional Neural Networks
Abstract
Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization and initialization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also more robust with regard to face location changes and scale variations when compared to classical methods such as e.g. MLPs. Our approach is based on convolutional neural networks that use multi-scale feature extractors, which allow for improved facial expression recognition results with faces subject to in-plane pose variations.
Year
DOI
Venue
2002
10.1109/ICMI.2002.1167051
ICMI
Keywords
Field
DocType
multi-scale feature extractor,classical method,improved facial expression recognition,automatic face analysis,convolutional neural networks,convolutional neural network,data-driven face analysis approach,face analysis method,initialization procedure,head-pose invariant facial expression,location change,face analysis task,neural nets,data analysis,vision,face recognition,robustness,convolution,neural networks,feature extraction,neural network
Computer vision,Facial recognition system,Face hallucination,Three-dimensional face recognition,Pattern recognition,Convolutional neural network,Computer science,Feature extraction,Feature (machine learning),Artificial intelligence,Face detection,Artificial neural network
Conference
ISBN
Citations 
PageRank 
0-7695-1834-6
16
0.78
References 
Authors
7
1
Name
Order
Citations
PageRank
Beat Fasel1866.97