A comparative study of facial appearance modeling methods for active appearance models

Authors:
Sung Joo Lee;Kang Ryoung Park;Jaihie Kim
Affiliations:
School of Electrical and Electronic Engineering, Yonsei University, Biometrics Engineering Research Center, Seoul 120-749, Republic of Korea;Department of Electronics Engineering, Dongguk University, Biometrics Engineering Research Center, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Biometrics Engineering Research Center, Seoul 120-749, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2009

Citing 14
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Non-negative Matrix Factorization with Sparseness Constraints

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Abstract

Active appearance models (AAMs) have been widely used in many face modeling and facial feature extraction methods. One of the problems of AAMs is that it is difficult to model a sufficiently wide range of human facial appearances, the pattern of intensities across a face image patch. Previous researches have used principal component analysis (PCA) for facial appearance modeling, but there has been little analysis and comparison between PCA and many other facial appearance modeling methods such as non-negative matrix factorization (NMF), local NMF (LNMF), and non-smooth NMF (ns-NMF). The main contribution of this paper is to find a suitable facial appearance modeling method for AAMs by a comparative study. In the experiments, PCA, NMF, LNMF, and ns-NMF were used to produce the appearance model of the AAMs and the root mean square (RMS) errors of the detected feature points were analyzed using the AR and BERC face databases. Experimental results showed that (1) if the appearance variations of testing face images were relatively non-sparser than those of training face images, the non-sparse methods (PCA, NMF) based AAMs outperformed the sparse methods (nsNMF, LNMF) based AAMs. (2) If the appearance variations of testing face images are relatively sparser than those of training face images, the sparse methods (nsNMF) based AAMs outperformed the non-sparse methods (PCA, NMF) based AAMs.