计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
9期
135-140
,共6页
鲁棒人脸识别%自适应直方图均衡化%离散余弦变换%系数重变换%核主成分分析
魯棒人臉識彆%自適應直方圖均衡化%離散餘絃變換%繫數重變換%覈主成分分析
로봉인검식별%자괄응직방도균형화%리산여현변환%계수중변환%핵주성분분석
robust face recognition%adaptive histogram equalization%Discrete Cosine Transform(DCT)%coefficients retransforming%Kernel Principal Component Analysis(KPCA)
针对光照、表情、姿态、遮挡等变化显著影响人脸识别系统性能的问题，提出了基于限制对比度自适应直方图均衡化（CLAHE）的低频离散余弦变换（DCT）系数重变换算法。将图像划分成多个互不重叠的局部小块，使用CLAHE对每个局部小块进行局部对比拉伸以实现去噪，通过缩减适当数目的低频DCT系数来消除人脸图像中的光照变化；利用核主成分分析进行特征提取，采用K-最近邻分类器以完成最终的人脸识别。在ORL、扩展YaleB和AR人脸数据库上的实验验证了所提算法的有效性和鲁棒性，实验结果表明，相比其他几种较为先进的人脸识别技术，所提算法取得了更高的识别率，同时大大降低了识别所用时间。
針對光照、錶情、姿態、遮擋等變化顯著影響人臉識彆繫統性能的問題，提齣瞭基于限製對比度自適應直方圖均衡化（CLAHE）的低頻離散餘絃變換（DCT）繫數重變換算法。將圖像劃分成多箇互不重疊的跼部小塊，使用CLAHE對每箇跼部小塊進行跼部對比拉伸以實現去譟，通過縮減適噹數目的低頻DCT繫數來消除人臉圖像中的光照變化；利用覈主成分分析進行特徵提取，採用K-最近鄰分類器以完成最終的人臉識彆。在ORL、擴展YaleB和AR人臉數據庫上的實驗驗證瞭所提算法的有效性和魯棒性，實驗結果錶明，相比其他幾種較為先進的人臉識彆技術，所提算法取得瞭更高的識彆率，同時大大降低瞭識彆所用時間。
침대광조、표정、자태、차당등변화현저영향인검식별계통성능적문제，제출료기우한제대비도자괄응직방도균형화（CLAHE）적저빈리산여현변환（DCT）계수중변환산법。장도상화분성다개호불중첩적국부소괴，사용CLAHE대매개국부소괴진행국부대비랍신이실현거조，통과축감괄당수목적저빈DCT계수래소제인검도상중적광조변화；이용핵주성분분석진행특정제취，채용K-최근린분류기이완성최종적인검식별。재ORL、확전YaleB화AR인검수거고상적실험험증료소제산법적유효성화로봉성，실험결과표명，상비기타궤충교위선진적인검식별기술，소제산법취득료경고적식별솔，동시대대강저료식별소용시간。
The performance of face recognition is seriously impacted by illumination, expression, posture and occlusion variations, for which low frequency Discrete Cosine Transform(DCT)coefficients retransforming based on Contrast Lim-iting Adaptive Histogram Equalization(CLAHE) is proposed. Original images are divided into some non-overlapping patches and CLAHE is used to do local contrast stretching so as to reduce noise. Illustration variation of face image is removed by reducing suit numbers of low frequency DCT coefficients. Kernel principle component analysis is used to extract features. Nearest neighbor classifier is used to finish classification and recognition. The effectiveness and reliability of proposed algorithm have been verified by experiments on ORL,extended YaleB and AR face database. Experimental results show that proposed algorithm has higher recognition accuracy than several advanced standardized technologies.
