Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/10536
Title: | Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features | Authors: | Pantraki, Evangelia Kotropoulos, Constantine L. Lanitis, Andreas |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Keywords: | PARAFAC2;Automatic prediction;TCDSA database | Issue Date: | 1-Jul-2017 | Source: | IET Biometrics, 2017, vol. 6, no. 4, pp. 290-298 | Volume: | 6 | Issue: | 4 | Start page: | 290 | End page: | 298 | 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: | https://hdl.handle.net/20.500.14279/10536 | ISSN: | 20474938 | DOI: | 10.1049/iet-bmt.2016.0122 | Rights: | © The Institution of Engineering and Technology | Type: | Article | Affiliation : | Cyprus University of Technology Aristotle University of Thessaloniki |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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