New face expression recognition using polar angular radial transform and principal component analysis
by Imène Taleb; Madani Ould Mammar; Abdelaziz Ouamri
International Journal of Biometrics (IJBM), Vol. 10, No. 2, 2018

Abstract: This paper presents a new method for facial expression recognition (FER) using a polar mathematical development based on the angular radial transformation (ART). This method is combined by polar angular radial transform (P-ART) and principal component analysis (PCA). The new ART is a powerful descriptor in terms of robustness, description form and way more information-rich compared to the conventional Cartesian descriptor. Support vector machine (SVM) training is used to recognise the facial expression for an input face image. Finally, the experimental results show the performance of the P-ART and the PCA. The fusion of these two techniques can be better than other existing methods of recognition of facial expression. During the experiment, the basis of facial given Japanese female facial expression (JAFFE) and the Cohn-Kanade databases has been used.

Online publication date: Tue, 08-May-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com