Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Comparison of PCA, LDA, and Matching Methods for Face Recognition

얼굴인식을 위한 PCA, LDA 및 정합기법의 비교

  • 박세제 (경희대학교 전자공학과) ;
  • 박영태 (경희대학교 전자공학과)
  • Published : 2003.04.01
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Abstract

Limitations on the linear discriminant analysis (LDA) for face rerognition, such as the loss of generalization and the computational infeasibility, are addressed and illustrated for a small number of samples. The principal component analysis (PCA) followed by the LDA mapping may be an alternative that ran overcome these limitations. We also show that any schemes based on either mappings or template matching are vulnerable to image variations due to rotation, translation, facial expressions, or local illumination conditions. This entails the importance of a proper preprocessing that can compensate for such variations. A simple template matching, when combined with the geometrically correlated feature-based detection as a preprocessing, is shown to outperform mapping techniques in terms of both the accuracy and the robustness to image variations.

얼굴 인식을 위한 주요 기법인 PCA, LBA 등과 같은 mapping에 의한 기법과 템플리트 정합기법 모두 얼굴 영역의 회전, 이동, 표정, 그리고 조명조건의 변화에 민감한 특성을 가진다. 본 논문에서는, 영상의 변화를 보상할 수 있는 전처리 과정으로서 기하학적 특징에 기반한 순수 얼굴영역검출기법을 도입하고 후처리 과정으로서 간단한 정합기법을 사용한 얼굴인식 기법을 제안한다. 제안한 기법은 PCA와 LDA 기법에 비해 영상의 변화에 민감하지 않고 높은 인식률을 보장할 수 있는 장점을 가진다.

Keywords

References

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