A Fast and Robust Feature Set for Cross Individual Facial Expression Recognition View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Rodrigo Araujo , Yun-Qian Miao , Mohamed S. Kamel , Mohamed Cheriet

ABSTRACT

This paper presents a new simple and robust set of features to classify emotional states in sequences of facial images. The proposed method is derived from simple geometric-based features that deliver a fast, highly discriminative, low-dimensional, and robust classification across individuals. The proposed method was compared to other state-of-the-art methods such as Gabor, LBP and AAM-based features. They were all compared using four different classifiers and experimental results based on these classifiers have shown that the proposed features are more stable in “leave-same-sequence-image-out” (LSSIO) environments, less computational intense and faster when compared to others. More... »

PAGES

272-279

Book

TITLE

Computer Vision and Graphics

ISBN

978-3-642-33563-1
978-3-642-33564-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33564-8_33

DOI

http://dx.doi.org/10.1007/978-3-642-33564-8_33

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1011191421


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