Conference Paper

Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face.

DOI: 10.1007/978-3-540-69812-8_43 Conference: Image Analysis and Recognition, 5th International Conference, ICIAR 2008, Póvoa de Varzim, Portugal, June 25-27, 2008. Proceedings
Source: DBLP

ABSTRACT In this paper, we propose a new feature extraction method for regression problems. It is a modified version of linear discriminant
analysis (LDA) which is a very successful feature extraction method for classification problems. In the proposed method, the
between class and the within class scatter matrices in LDA are modified so that they fit in regression problems. The samples
with small differences in the target values are used to constitute the within class scatter matrix while the ones with large
differences in the target values are used for the between class scatter matrix. We have applied the proposed method in estimating
the head pose and compared the performance with the conventional feature extraction methods.

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    ABSTRACT: We present an image-based method for face recognition across different illuminations and poses, where the term image-based means that no explicit prior three-dimensional models are needed. As face recognition un- der illumination and pose variations involves three factors, namely, identity, illumination, and pose, generali- zations in all these three factors are desired. We present a recognition approach that is able to generalize in the identity and illumination dimensions and handle a given set of poses. Specifically, the proposed approach derives an identity signature that is illumination- and pose-invariant, where the identity is tackled by means of subspace encoding, the illumination is characterized with a Lambertian reflectance model, and the given set of poses is treated as a whole. Experimental results using the Pose, Illumination, and Expression (PIE) da- tabase demonstrate the effectiveness of the proposed approach. © 2005 Optical Society of America OCIS codes: 150.2950, 100.5010.
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