电子设计工程
電子設計工程
전자설계공정
ELECTRONIC DESIGN ENGINEERING
2014年
14期
120-122,127
,共4页
人脸识别%K-L交叉熵%PCA%岭回归
人臉識彆%K-L交扠熵%PCA%嶺迴歸
인검식별%K-L교차적%PCA%령회귀
face recognition%K-L cross entropy%Principal Component Analysis(PCA)%ridge regression
岭回归人脸识别利用正则单形的顶点对每类人脸进行多元标记，通过投影实现高维人脸特征的降维。该算法首先提取人脸图像的局部二进制（LBP）直方图特征向量,通过主成分分析（PCA）和岭回归对该特征向量进行两次降维。识别阶段利用K-L交叉熵计算标记向量和投影后特征向量的相似性，根据熵值最小原则完成对测试样本的类别判断。实验选取ORL和YALE两个标准人脸库对算法进行测试，结果表明，K-L交叉熵测度比传统的欧氏距离测度获得更高的识别率。
嶺迴歸人臉識彆利用正則單形的頂點對每類人臉進行多元標記，通過投影實現高維人臉特徵的降維。該算法首先提取人臉圖像的跼部二進製（LBP）直方圖特徵嚮量,通過主成分分析（PCA）和嶺迴歸對該特徵嚮量進行兩次降維。識彆階段利用K-L交扠熵計算標記嚮量和投影後特徵嚮量的相似性，根據熵值最小原則完成對測試樣本的類彆判斷。實驗選取ORL和YALE兩箇標準人臉庫對算法進行測試，結果錶明，K-L交扠熵測度比傳統的歐氏距離測度穫得更高的識彆率。
령회귀인검식별이용정칙단형적정점대매류인검진행다원표기，통과투영실현고유인검특정적강유。해산법수선제취인검도상적국부이진제（LBP）직방도특정향량,통과주성분분석（PCA）화령회귀대해특정향량진행량차강유。식별계단이용K-L교차적계산표기향량화투영후특정향량적상사성，근거적치최소원칙완성대측시양본적유별판단。실험선취ORL화YALE량개표준인검고대산법진행측시，결과표명，K-L교차적측도비전통적구씨거리측도획득경고적식별솔。
Ridge regression for face recognition uses the vertices of a regular simplex to encode the multiple labels for each face,and maps the high-dimensional feature into a low-dimension subspace. This algorithm firstly extracts the feature vector of LBP histogram in a face image. Principal component analysis (PCA) and ridge regression are used successively to reduce the dimension twice. In face recognition stage, K-L cross entropy is utilized to calculate the similarity between the label vector and projected feature vector. The principle of minimum entropy can determine the category which the test sample belongs to. ORL and YALE face databases are selected to test the algorithm. Experimental results demonstrate that K-L cross entropy will get higher recognition rate than traditional Euclidean distance.
