Open AccessAge interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features
Author(s) -
Pantraki Evangelia,
Kotropoulos Constantine,
Lanitis Andreas
Publication year - 2017
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2016.0122
Subject(s) - support vector machine , computer science , artificial intelligence , pattern recognition (psychology) , benchmark (surveying) , ranking (information retrieval) , classifier (uml) , interval (graph theory) , speech recognition , audio visual , machine learning , mathematics , multimedia , combinatorics , geodesy , geography
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.