计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
22期
163-167
,共5页
双向二维局部保持映射%线性判别式分析%人脸识别%计算复杂度
雙嚮二維跼部保持映射%線性判彆式分析%人臉識彆%計算複雜度
쌍향이유국부보지영사%선성판별식분석%인검식별%계산복잡도
discriminant bidirectional two-dimensional local preserving projection%linear discriminant analysis%face recognition%calculation complexity
双向二维局部保持映射(双向2DLPP)与二维局部保持映射(2DLPP)比较,双向2DLPP同时对图像的行方向和列方向进行降维处理,可以采用较少的系数有效地表示图像。为了进一步增强双向2DLPP算法的分类能力,将双向2DLPP所提取的特征采用线性判别式分析(LDA)进行分类,从而形成了一种新的监督算法:鉴别双向二维局部保持投影。理论分析表明,无论在计算量还是内存要求方面,所提鉴别双向二维局部保持投影算法比双向2DLPP和主成分分析+线性判别式分析(PCA+LDA)要少,而且在ORL 和Yale数据库上的人脸识别实验表明,新算法的识别性能比双向2DLPP和PCA+LDA算法要好,且具有较少的计算复杂度。
雙嚮二維跼部保持映射(雙嚮2DLPP)與二維跼部保持映射(2DLPP)比較,雙嚮2DLPP同時對圖像的行方嚮和列方嚮進行降維處理,可以採用較少的繫數有效地錶示圖像。為瞭進一步增彊雙嚮2DLPP算法的分類能力,將雙嚮2DLPP所提取的特徵採用線性判彆式分析(LDA)進行分類,從而形成瞭一種新的鑑督算法:鑒彆雙嚮二維跼部保持投影。理論分析錶明,無論在計算量還是內存要求方麵,所提鑒彆雙嚮二維跼部保持投影算法比雙嚮2DLPP和主成分分析+線性判彆式分析(PCA+LDA)要少,而且在ORL 和Yale數據庫上的人臉識彆實驗錶明,新算法的識彆性能比雙嚮2DLPP和PCA+LDA算法要好,且具有較少的計算複雜度。
쌍향이유국부보지영사(쌍향2DLPP)여이유국부보지영사(2DLPP)비교,쌍향2DLPP동시대도상적행방향화렬방향진행강유처리,가이채용교소적계수유효지표시도상。위료진일보증강쌍향2DLPP산법적분류능력,장쌍향2DLPP소제취적특정채용선성판별식분석(LDA)진행분류,종이형성료일충신적감독산법:감별쌍향이유국부보지투영。이론분석표명,무론재계산량환시내존요구방면,소제감별쌍향이유국부보지투영산법비쌍향2DLPP화주성분분석+선성판별식분석(PCA+LDA)요소,이차재ORL 화Yale수거고상적인검식별실험표명,신산법적식별성능비쌍향2DLPP화PCA+LDA산법요호,차구유교소적계산복잡도。
Recently, bidirectional two-dimensional Local Preserving Projection(2DLPP)is proposed for face representa-tion and recognition. Compared with two-dimensional Local Preserving Projection(2DLPP), the main idea behind bidirec-tional 2DLPP is that bidirectional 2DLPP simultaneously considering the row and column directions of images. Bidirec-tional 2DLPP needs fewer coefficients for image representation than 2DLPP which essentially works in the row direction of images. Furthermore, to enhance the classification ability of bidirectional 2DLPP, a new supervised algorithm is pro-posed:bidirectional 2DLPP plus LDA, in which images preprocessed by bidirectional 2DLPP are processed by LDA. The-oretical analyses show that bidirectional 2DLPP plus LDA algorithm has advantages over PCA+LDA, 2DLPP and bidirec-tional 2DLPP in computation complexity and memory requirements. The results of face recognition experimental on ORL and Yale face databases also show good performance of the methods with less computation.