Appearance-based gender classification with Gaussian processes
SCIE
SCOPUS
- Title
- Appearance-based gender classification with Gaussian processes
- Authors
- Hyun-Chul Kim; Kim, D; Zoubin Ghahramani; Sung Yang Bang
- Date Issued
- 2006-04-15
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. Recently.. support vector machines (SVMs) which are popular kernel classifiers have been applied to gender classification and have shown excellent performance. SVMs have difficulty in determining the hyperparameters in kernels (using cross-validation). We propose to use Gaussian process classifiers (GPCs) which are Bayesian kernel classifiers. The main advantage of GPCs over SVMs is that they determine the hyperparameters of the kernel based on Bayesian model selection criterion. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets. Moreover, the kernel hyperparameters found by GPCs using Bayesian methods call be used to improve SVM performance. (c) 2005 Elsevier B.V. All rights reserved.
- Keywords
- gender classification; appearance-based gender classification; kernel machines; Gaussian process classifiers; support vector machines; FACES
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/24123
- DOI
- 10.1016/j.patrec.2005.09.027
- ISSN
- 0167-8655
- Article Type
- Article
- Citation
- PATTERN RECOGNITION LETTERS, vol. 27, no. 6, page. 618 - 626, 2006-04-15
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.