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


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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Available from: Nojun Kwak, Oct 07, 2015
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    • "where alpha = 0.1 as in [9]. "
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    ABSTRACT: In this paper, we compared the performance of various combinations of edge operators and linear subspace methods to determine the best combination for pose classification. To evaluate the performance, we have carried out experiments on CMU-PIE database which contains images with wide variation in illumination and pose. We found that the performance of pose classification depends on the choice of edge operator and linear subspace method. The best classification accuracy is obtained with Prewitt edge operator and Eigenfeature regularization method. In order to handle illumination variation, we used adaptive histogram equalization as a preprocessing step resulting into significant improvement in performance except for Roberts operator.
    International Journal of Computer Applications 01/2012; 37(1):14-19. DOI:10.5120/4571-6565
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    • ") and the Principal Hessian Directions (PHD) (Li, 1992), along with the conventional LDA which has been very successful for classification problems and LDA-r (Kwak et al., 2008), which is a variant of LDA to effectively handle classification problems with order relationship between classes. When the pixels of an image are used as input variables of a 12,000-dimensional input space, and the Small Sample Size (SSS) problem occurs in extracting the features for pose estimation. "
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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.
    Pattern Recognition Letters 03/2011; 32(4):561-571. DOI:10.1016/j.patrec.2010.11.021 · 1.55 Impact Factor