CAJ | 학술논문

由于传统的PCA要求训练样本符合高斯分布，而现实中得到的图片往往由于光照、表情、姿态的不同，不符合高斯分布。为了使PCA不再局限于高斯分布，并且不影响其识别率，提出一种改进的模块PCA人脸识别新算法。一方面，新算法采取了分块方式，将具有同一姿态的图片划分进同一矩阵，以使训练样本更接近于高斯分布。另一方面，新算法对传统PCA算法中前三个主分量加小于1的权重系数，可以减少光照变化对识别率的影响。利用分块和权重系数的共同作用使得PCA不再局限于高斯分布，同时提高识别率。最后在ORL人脸库上进行实验，结果表明新算法优于传统的PCA算法。
유우전통적PCA요구훈련양본부합고사분포，이현실중득도적도편왕왕유우광조、표정、자태적불동，불부합고사분포。위료사PCA불재국한우고사분포，병차불영향기식별솔，제출일충개진적모괴PCA인검식별신산법。일방면，신산법채취료분괴방식，장구유동일자태적도편화분진동일구진，이사훈련양본경접근우고사분포。령일방면，신산법대전통PCA산법중전삼개주분량가소우1적권중계수，가이감소광조변화대식별솔적영향。이용분괴화권중계수적공동작용사득PCA불재국한우고사분포，동시제고식별솔。최후재ORL인검고상진행실험，결과표명신산법우우전통적PCA산법。
The traditional Principal Component Analysis(PCA)requires that training samples are in accordance with Gauss-ian distribution strictly. However, generating pictures are always influenced by illumination, facial expressions, and pos-tures. In order to solve the problem, a new modular algorithm based on PCA is proposed, which is also a guarantee of the rate of identification. The new algorithm, on the one hand, takes a blocked mode which divides pictures with a same pos-ture into one matrix, so the training sample can be closer to the Gaussian distribution. On the other hand, since the first three characteristics of the principal component are easily affected by light variation, a less than one weighting coefficient is added to reduce the effects of light in the recognition. Thus the improved PCA training matrix is no longer limited to the Gaussian distribution with the combinations of the sub-blocks and the weight coefficients, the recognition rate is improved at the same time. The numerical experiments in the ORL human face databases show that the improved algorithm is supe-rior to the traditional PCA algorithm.