CAJ | 학술논문

针对复杂光照条件下的人脸识别，提出了一种基于光照归一化分块完备局部二值模式（B-CLBP）特征的人脸识别算法。该方法对人脸图像进行光照归一化预处理，对处理后的人脸图像进行B-CLBP特征提取，融合成B-CLBP直方图，根据最近邻准则进行分类识别。在Extended Yale B人脸库上的实验结果表明，所提算法可以有效提高复杂光照条件下的人脸识别率。
침대복잡광조조건하적인검식별，제출료일충기우광조귀일화분괴완비국부이치모식（B-CLBP）특정적인검식별산법。해방법대인검도상진행광조귀일화예처리，대처리후적인검도상진행B-CLBP특정제취，융합성B-CLBP직방도，근거최근린준칙진행분류식별。재Extended Yale B인검고상적실험결과표명，소제산법가이유효제고복잡광조조건하적인검식별솔。
Aiming at the problem of face recognition under complex illumination, an effective face recognition method based on illumination normalization and block-based completed local binary pattern is proposed. The method performs illumination normalization on face images, then extracts the B-CLBP features from the processed face images in order to form the B-CLBP histogram, applies the nearest neighbor principle for face recognition. The experimental results on Extended Yale B face databases demonstrate that the proposed method can achieve significant recognition rate under com-plex illumination conditions.