三维及多模态人脸识别研究
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摘要
目前的二维人脸识别系统在受控条件下能取得很好的性能,但在光照、姿态、表情等因素影响下性能将急剧下降。三维人脸识别可以克服或减轻这些因素的影响。融合二维和三维信息的多模态人脸识别可望取得更好的识别效果。本文对三维及多模态人脸识别的若干算法进行了研究。
首先提出了一个基于迭代对应点(ICP)的三维人脸识别方法。先通过聚类算法去除人脸点云的局外点,再以鼻尖为中心提取感兴趣区域,并变换到姿态坐标系进行粗略配准。利用人脸对称性填补孔洞,提高了人脸数据的质量。再用ICP算法进行精细配准,采用最近邻分类器进行分类。实验结果表明该方法能够处理一定程度的人脸姿态变化,即便人脸数据的质量不高,仍能取得较好的识别效果。
提出了一种将三维局部二值模式(3DLBP)和广义判别分析(GDA)相结合的三维人脸识别算法。将人脸深度图像分成多个区域后,采用3DLBP算子从各区域提取直方图特征,并将各区域3DLBP直方图连成一个向量,作为人脸深度图像的特征,采用改进高斯核函数的GDA作为分类器。实验结果表明,3DLBP和GDA结合的识别率要优于PCA和3DLBP。
采用多种方法对人脸深度图像和灰度图像进行融合。对LBP算子和局部Gabor二值模式(LGBP)算子进行了详细的比较。实验结果表明LBP和Fisher判别分析(FDA)方法的结合要优于其它方法,在其融合人脸深度图像和灰度图像后,性能较单一信息有进一步的提升。基于LGBP的各方法与基于LBP的相应方法相比,在计算量和存储量上要大很多,但在性能上却没有优势。
提出了一种基于LBP和级联AdaBoost的多模态人脸识别方法。采用级联AdaBoost方法分别从人脸深度图像和灰度图像的大量区域LBP直方图(RLBPH)中选取最有利于分类的RLBPH,并连接成一个直方图向量。分别用FDA构建线性子空间,再用多种方法进行融合。实验结果表明,级联AdaBoost选出的少量RLBPH特征取得了较好的识别效果,若增加特征数,则可进一步提高识别性能。
2D face recognition system can achieve good performances under controlled conditions. But its performance will drop drastically under the influence of some factors, such as illumination, pose, and expression variations. 3D face recognition can overcome or alleviate the influence of these factors. Multi-modal face recognition combined 2D with 3D information can be expected to obtain better performance. Several algorithms on 3D and multi-modal face recognition have been investigated in this dissertation.
Firstly a 3D face recognition method based on iterative corresponding point (ICP) is presented. A clustering algorithm is proposed to eliminate point outliers from the facial point cloud. Then, the region of interest of the facial point cloud is extracted and transformed to pose coordinate system for coarse alignment. An approach based on symmetry property of facial surface is used to fill the holes of the facial data so as to improve the quality of facial data. And ICP algorithm is employed for fine registration. Finally, nearest neighbor classifier is adopted as the evaluation method. Experimental results demonstrate that the proposed algorithm have the capability of handling facial pose variation to some extent. The performance is still fairly good even when the facial data are of poor quality.
A method of 3D face recognition which combines 3D local binary pattern (3DLBP) descriptor with generalized discriminant analysis (GDA) is proposed. Firstly a facial depth image is divided into regions. 3DLBP is used to extract histograms from these regions. All regional 3DLBP histograms are concatenated to a vector which is used as the feature of the facial depth image. GDA with modified Gaussian kernel is adopted as the classifier. Experimental results show that the recognition rate of 3DLBP combining with GDA is better than PCA and 3DLBP.
Different fusion methods are used to combine facial depth images and grayscale images. LBP and local Gabor binary pattern (LGBP) are compared in detail. Experimental results illustrate that the combination of LBP and Fisher discrimiant analysis (FDA) is better than other methods. The performance after fusing facial depth image and grayscale images is better than that of unimodal ones. Methods based on LGBP cost more computation time and storage space, but have no advantages in performance compared with LBP based ones.
A method which combined LBP descriptor with chain AdaBoost is presented for multi-modal face recognition. Thousands of regional LBP histograms (RLBPH) are generated from facial depth images and grayscale images respectively. Chain AdaBoost is utilized to select most informative RLBPHs. The selected RLBPHs are concatenated to a whole histogram. Then the corresponding linear subspaces are constructed by FDA respectively. Several methods are used to fuse 2D and 3D information. The experimental results demonstrate that very few RLBPHs selected by chain AdaBoost achieve fairly good performance. The performance will be improved further as the number of features increases.
首先提出了一个基于迭代对应点(ICP)的三维人脸识别方法。先通过聚类算法去除人脸点云的局外点,再以鼻尖为中心提取感兴趣区域,并变换到姿态坐标系进行粗略配准。利用人脸对称性填补孔洞,提高了人脸数据的质量。再用ICP算法进行精细配准,采用最近邻分类器进行分类。实验结果表明该方法能够处理一定程度的人脸姿态变化,即便人脸数据的质量不高,仍能取得较好的识别效果。
提出了一种将三维局部二值模式(3DLBP)和广义判别分析(GDA)相结合的三维人脸识别算法。将人脸深度图像分成多个区域后,采用3DLBP算子从各区域提取直方图特征,并将各区域3DLBP直方图连成一个向量,作为人脸深度图像的特征,采用改进高斯核函数的GDA作为分类器。实验结果表明,3DLBP和GDA结合的识别率要优于PCA和3DLBP。
采用多种方法对人脸深度图像和灰度图像进行融合。对LBP算子和局部Gabor二值模式(LGBP)算子进行了详细的比较。实验结果表明LBP和Fisher判别分析(FDA)方法的结合要优于其它方法,在其融合人脸深度图像和灰度图像后,性能较单一信息有进一步的提升。基于LGBP的各方法与基于LBP的相应方法相比,在计算量和存储量上要大很多,但在性能上却没有优势。
提出了一种基于LBP和级联AdaBoost的多模态人脸识别方法。采用级联AdaBoost方法分别从人脸深度图像和灰度图像的大量区域LBP直方图(RLBPH)中选取最有利于分类的RLBPH,并连接成一个直方图向量。分别用FDA构建线性子空间,再用多种方法进行融合。实验结果表明,级联AdaBoost选出的少量RLBPH特征取得了较好的识别效果,若增加特征数,则可进一步提高识别性能。
2D face recognition system can achieve good performances under controlled conditions. But its performance will drop drastically under the influence of some factors, such as illumination, pose, and expression variations. 3D face recognition can overcome or alleviate the influence of these factors. Multi-modal face recognition combined 2D with 3D information can be expected to obtain better performance. Several algorithms on 3D and multi-modal face recognition have been investigated in this dissertation.
Firstly a 3D face recognition method based on iterative corresponding point (ICP) is presented. A clustering algorithm is proposed to eliminate point outliers from the facial point cloud. Then, the region of interest of the facial point cloud is extracted and transformed to pose coordinate system for coarse alignment. An approach based on symmetry property of facial surface is used to fill the holes of the facial data so as to improve the quality of facial data. And ICP algorithm is employed for fine registration. Finally, nearest neighbor classifier is adopted as the evaluation method. Experimental results demonstrate that the proposed algorithm have the capability of handling facial pose variation to some extent. The performance is still fairly good even when the facial data are of poor quality.
A method of 3D face recognition which combines 3D local binary pattern (3DLBP) descriptor with generalized discriminant analysis (GDA) is proposed. Firstly a facial depth image is divided into regions. 3DLBP is used to extract histograms from these regions. All regional 3DLBP histograms are concatenated to a vector which is used as the feature of the facial depth image. GDA with modified Gaussian kernel is adopted as the classifier. Experimental results show that the recognition rate of 3DLBP combining with GDA is better than PCA and 3DLBP.
Different fusion methods are used to combine facial depth images and grayscale images. LBP and local Gabor binary pattern (LGBP) are compared in detail. Experimental results illustrate that the combination of LBP and Fisher discrimiant analysis (FDA) is better than other methods. The performance after fusing facial depth image and grayscale images is better than that of unimodal ones. Methods based on LGBP cost more computation time and storage space, but have no advantages in performance compared with LBP based ones.
A method which combined LBP descriptor with chain AdaBoost is presented for multi-modal face recognition. Thousands of regional LBP histograms (RLBPH) are generated from facial depth images and grayscale images respectively. Chain AdaBoost is utilized to select most informative RLBPHs. The selected RLBPHs are concatenated to a whole histogram. Then the corresponding linear subspaces are constructed by FDA respectively. Several methods are used to fuse 2D and 3D information. The experimental results demonstrate that very few RLBPHs selected by chain AdaBoost achieve fairly good performance. The performance will be improved further as the number of features increases.
引文
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