A comparative study of feature extraction using PCA and LDA for face recognition

Erwin Hidayat, Nur A. Fajrian, A. Muda, Y. Choo, S. Ahmad
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引用次数: 28

Abstract

Feature extraction is important in face recognition. This paper presents a comparative study of feature extraction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. The evaluation parameters for the study are time and accuracy of each method. The experiments were conducted using six datasets of face images with different disturbance. The results showed that LDA is much better than PCA in overall image with various disturbances. While in time taken evaluation, PCA is faster than LDA.
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基于PCA和LDA的特征提取在人脸识别中的比较研究
特征提取是人脸识别的重要内容。本文对主成分分析(PCA)和线性判别分析(LDA)在人脸识别中的特征提取进行了比较研究。本研究的评价参数为每种方法的时间和准确性。实验采用6组不同干扰条件下的人脸图像进行。结果表明,在具有多种干扰的整体图像中,LDA的效果明显优于PCA。而在时间评价上,PCA比LDA更快。
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