2012 ©
Journal Publication
Research Title Analyze Facial Expression Recognition Based on Curvelet Transform via Extreme Learning Machine 
Date of Distribution 12 May 2019 
     Title of the Conference The 15th International Conference on Computing and Information Technology 
     Organiser มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ 
     Conference Place โรงแรม Arnoma Grand 
     Province/State กรุงเทพมหานคร 
     Conference Date 4 July 2019 
     To 5 July 2019 
Proceeding Paper
     Volume 936 
     Page 148-158 
     Editors/edition/publisher Springer 
     Abstract This paper aims to investigate the key factors of facial expression recognition based on local curvelet transform for real-time training data. Local curvelet transform (LCT) is the application of curvelet transform that benefits from useful features extracted by curvelet transform and reduces the computation cost of using all curvelet coefficients. The reduction of computation is through calculating the representative features, instead of directly using all curvelet coefficients. The representative features are mean, standard deviation and entropy. This approach has been reported to achieve impressively 0.9445 and 0.9486 accuracy on JAFFE and Cohn-Kanade datasets. However, there are many factors influencing the final performance, in which these factors have not been thoroughly studied. Our investigation has shown that these factors could result up to almost 10% difference and their effects are thoroughly studied. 
597040049-9 Mr. SARUTTE ATSAWARAUNGSUK [Main Author]
Engineering Doctoral Degree

Peer Review Status มีผู้ประเมินอิสระ 
Level of Conference นานาชาติ 
Type of Proceeding Full paper 
Type of Presentation Oral 
Part of thesis true 
Presentation awarding false 
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