计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
19期
156-160
,共5页
鲁棒人脸识别%光照变化%局部线性嵌入%光谱回归%流形学习
魯棒人臉識彆%光照變化%跼部線性嵌入%光譜迴歸%流形學習
로봉인검식별%광조변화%국부선성감입%광보회귀%류형학습
robust face recognition%illumination variation%local linear embedding%spectral regression%manifold learning
针对高维小样本鲁棒人脸识别问题，提出了一种局部线性嵌入优化光谱回归算法。计算出训练样本的特征向量，然后用局部线性嵌入算法构建分类问题所需的嵌入，并学习每种分类的子流形所需的嵌入；利用光谱回归计算投影矩阵，最近邻分类器完成人脸的识别。在人脸数据库FERET、AR及扩展YaleB上的实验结果表明，相比其他几种光谱回归算法，该算法取得了更好的识别效果。
針對高維小樣本魯棒人臉識彆問題，提齣瞭一種跼部線性嵌入優化光譜迴歸算法。計算齣訓練樣本的特徵嚮量，然後用跼部線性嵌入算法構建分類問題所需的嵌入，併學習每種分類的子流形所需的嵌入；利用光譜迴歸計算投影矩陣，最近鄰分類器完成人臉的識彆。在人臉數據庫FERET、AR及擴展YaleB上的實驗結果錶明，相比其他幾種光譜迴歸算法，該算法取得瞭更好的識彆效果。
침대고유소양본로봉인검식별문제，제출료일충국부선성감입우화광보회귀산법。계산출훈련양본적특정향량，연후용국부선성감입산법구건분류문제소수적감입，병학습매충분류적자류형소수적감입；이용광보회귀계산투영구진，최근린분류기완성인검적식별。재인검수거고FERET、AR급확전YaleB상적실험결과표명，상비기타궤충광보회귀산법，해산법취득료경호적식별효과。
For the robust face recognition problem with high-dimensional small sample, the algorithm of spectral regression classification optimized by local linear embedding is proposed. Firstly, feature vectors of training samples are calculated. Then, local linear embedding is used to construct embedding needed by classification and embeddings needed by sub-manifold of each classification is learned. Finally, spectral regression classification algorithm is used to compute project metrics, and nearest neighbor classifier is used to recognize face. Experimental results on the common face datasets FERET, AR and Extended YaleB show that proposed algorithm has better recognition efficiency than several other spectral regression algorithms.
