Please use this identifier to cite or link to this item:
Title: Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features
Authors: Pantraki, Evangelia 
Kotropoulos, Constantine L. 
Lanitis, Andreas 
Keywords: PARAFAC2;Automatic prediction;TCDSA database
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 1-Jul-2017
Publisher: The Institution of Engineering and Technology
Source: IET BIOMETRICS, Volume: 6, Issue: 4, Pages: 290-298, Special Issue: SI, Published: JUL 2017
Journal: IET Biometrics
Abstract: Parallel factor analysis 2 (PARAFAC2) is employed to reduce the dimensions of visual and aural features and provide ranking vectors. Subsequently, score level fusion is performed by applying a support vector machine (SVM) classifier to the ranking vectors derived by PARAFAC2 to make gender and age interval predictions. The aforementioned procedure is applied to the Trinity College Dublin Speaker Ageing database, which is supplemented with face images of the speakers and two single-modality benchmark datasets. Experimental results demonstrate the advantage of using combined aural and visual features for both prediction tasks.
ISSN: 2047-4938
Rights: © The Institution of Engineering and Technology 2017
Type: Article
Appears in Collections:Άρθρα/Articles

Show full item record

Page view(s) 50

Last Week
Last month
checked on Dec 10, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.