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- Title
Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features.
- Authors
Pantraki, Evangelia; Kotropoulos, Constantine; Lanitis, Andreas
- 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 singlemodality benchmark datasets. Experimental results demonstrate the advantage of using combined aural and visual features for both prediction tasks.
- Subjects
SUPPORT vector machines; BIOMETRIC identification; DATABASES; PHYSICAL characteristics (Human body); GENDER
- Publication
IET Biometrics (Wiley-Blackwell), 2017, Vol 6, Issue 4, p290
- ISSN
2047-4938
- Publication type
Article
- DOI
10.1049/iet-bmt.2016.0122