Paper Info
Title
Automatic facial expression recognition using statistical-like moments
Abstract
Research in automatic facial expression recognition has permitted the development of systems discriminating between the six prototypical expressions, i.e. anger, disgust, fear, happiness, sadness and surprise, in frontal video sequences. Achieving high recognition rate often implies high computational costs that are not compatible with real time applications on limited-resource platforms. In order to have high recognition rate as well as computational efficiency, we propose an automatic facial expression recognition system using a set of novel features inspired by statistical moments. Such descriptors, named as statisticallike moments extract high order statistic from texture descriptors such as local binary patterns. The approach has been successfully tested on the second edition of Cohn-Kanade database, showing a computational advantage and achieving a performance recognition rate comparable than methods based on different descriptors
Year
DOI
Venue
2011
10.1007/978-3-642-24085-0_60
ICIAP (1)
Keywords
Field
DocType
statistical-like moment,high order statistic,prototypical expression,different descriptors,performance recognition rate,computational advantage,automatic facial expression recognition,cohn-kanade database,high computational cost,computational efficiency,high recognition rate
Sadness,Computer vision,Expression (mathematics),Three-dimensional face recognition,Pattern recognition,Computer science,Disgust,Local binary patterns,Artificial intelligence,Surprise,Order statistic,Method of moments (statistics)
Conference
Volume
ISSN
Citations 
6978
0302-9743
2
PageRank 
References 
Authors
0.37
15
3
Name
Order
Citations
PageRank
Roberto D'Ambrosio1132.30
Giulio Iannello241446.75
Paolo Soda340739.44