本文選題：光照不變特征表示 + 人臉識別　； 參考：《計算機工程與應用》2017年01期

【摘要】：在光照變化環(huán)境下,人臉識別的魯棒性是人臉識別系統(tǒng)中一大挑戰(zhàn)。針對光照變化對人臉識別的影響,對經(jīng)典光照不變特征表示算法進行了研究,提出一種基于局部標準差光照不變的人臉特征表示算法及其加權(quán)形式。結(jié)合完備線性鑒別分析(Complete-Linear Discriminant Analysis,C-LDA)算法提取特征,在Extended Yale-B與YALE人臉庫中,與其他處理光照變化的經(jīng)典方法相比,如多尺度Retinex(Multi Scale Retinex,MSR)、韋伯臉(Weber-Face,WF)和局部歸一化(Local Normalization,LN),提出的算法能獲得更高識別率。
[Abstract]:The robustness of face recognition is a major challenge in face recognition systems under varying illumination conditions. Aiming at the influence of illumination variation on face recognition, the classical illumination invariant feature representation algorithm is studied, and a face feature representation algorithm based on local standard deviation illumination invariance and its weighted form is proposed. Combined with the complete linear discriminant analysis (Complete-Linear discriminant Analysis) algorithm, the features are extracted. In extended Yale-B and Yale face databases, compared with other classical methods to deal with illumination changes, For example, multiscale Retinexy Multi scale Retinexan MSRs, Weber-Weber-FFs and Local NormalizationLNs, the proposed algorithm can obtain higher recognition rate.
【作者單位】： 暨南大學信息科學技術(shù)學院;暨南大學電氣信息學院;
【基金】：廣東省學科建設專項資金項目-科技創(chuàng)新(No.2013KJCX0023) 珠海市公共技術(shù)服務平臺科技項目(No.2013D0501990013)
【分類號】：TP391.41
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本文編號：2022743