CAJ | 학술논문

　　针对人脸图像易受光线和表情影响的特点，提出了一种基于二进小波变换和仿生模式识别的人脸识别方法。应用样条二进小波对人脸图像进行处理，对得到的细节子图进行融合。在FFT和PCA处理与降维后，用仿生模式识别进行学习和识别。实验结果表明，该方法比传统方法具有更高的识别率。
　　침대인검도상역수광선화표정영향적특점，제출료일충기우이진소파변환화방생모식식별적인검식별방법。응용양조이진소파대인검도상진행처리，대득도적세절자도진행융합。재FFT화PCA처리여강유후，용방생모식식별진행학습화식별。실험결과표명，해방법비전통방법구유경고적식별솔。
Aiming at robustness of face recognition under the condition of illumination perturbations and facial variety, a face recognition method based on dyadic wavelet transform and biomimetic pattern recognition is proposed in this paper. The spline dyadic wavelet is applied to face images. The detail subbands are merged by concatenation. The facial images are treated and reduced dimension respectively by FFT(Fast Fourier Transform)and PCA(Principal Component Analysis). The feature vectors are learned and recognized by biomimetic pattern recognition. The experimental results prove that the correct recognition rate of the method in this paper is higher than traditional recognition method.