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.
|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||metadata.dc.doi:||http://dx.doi.org/10.1049/iet-bmt.2016.0122||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.||URI:||http://ktisis.cut.ac.cy/handle/10488/10536||ISSN:||2047-4938||Rights:||© The Institution of Engineering and Technology 2017||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Dec 13, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.