A design for integrated face and facial expression recognition

Kai-Tai Song*, Yi Wen Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

An integrated face and facial expression recognition system has been designed and tested for robotic applications. Facial images from a web camera are first acquired for facial shape and texture model generation using active appearance model (AAM). Modified Lucas-Kanade image alignment algorithm was adopted to find facial features as well as the texture model of AAM to construct facial texture parameters. These parameters are used to train back propagation neural networks (BPNN) for face and facial expression recognition. A novel design is proposed for an integrated facial expression recognition system. In the first stage, face recognition is performed to find user's identity; then the facial-expression database of the recognized user is employed to recognize his/her facial expressions. Experimental result based on BU-3DFE database show that a face recognition rate of 98.3% is achieved. The facial expression recognition rate of the proposed integrated method (using personal facial expression classifiers) is 83.8%, an improvement compared with 69.6% of using conventional classifiers.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society
Pages4306-4311
Number of pages6
DOIs
StatePublished - 1 Dec 2011
Event37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011 - Melbourne, VIC, Australia
Duration: 7 Nov 201110 Nov 2011

Publication series

NameIECON Proceedings (Industrial Electronics Conference)

Conference

Conference37th Annual Conference of the IEEE Industrial Electronics Society, IECON 2011
CountryAustralia
CityMelbourne, VIC
Period7/11/1110/11/11

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