公开(公告)号：US09576224B2

公开(公告)日：2017-02-21

申请号：US14587562

申请日：2014-12-31

Applicant: TCL RESEARCH AMERICA INC.

Inventor： Michael Iliadis ,  Haohong Wang

IPC: G06K9/62 ,  G06K9/52 ,  G06K9/46 ,  G06K9/00

CPC classification number: G06K9/6256 ,  G06F21/32 ,  G06K9/00261 ,  G06K9/00288 ,  G06K9/00302 ,  G06K9/00765 ,  G06K9/4661 ,  G06K9/52 ,  G06K9/6249

Abstract: The present invention provides a face recognition method on a computing device, comprising: storing a plurality of training face images, each training face image corresponding to a face class; obtaining one or more face test samples; applying a representation model to represent the face test sample as a combination of the training face images and error terms, wherein a coefficient vector is corresponded to the training face images; estimating the coefficient vector and the error terms by solving a constrained optimization problem; computing a residual error for each face class, the residual error for a face class being an error between the face test sample and the face test sample's representation model represented by the training samples in the face class; classifying the face test sample by selecting the face class that yields the minimal residual error; and presenting the face class of the face test sample.

Abstract translation: 本发明提供了一种在计算设备上的面部识别方法，包括：存储多个训练面部图像，每个训练面部图像对应于面部类别; 获得一个或多个面部测试样本; 应用表示模型来表示面部测试样本作为训练面部图像和误差项的组合，其中系数向量对应于训练面部图像; 通过求解约束优化问题来估计系数向量和误差项; 计算每个面部类别的残差，面部类别的残差是面部测试样本与脸部类别中训练样本所代表的面部测试样本表示模型之间的误差; 通过选择产生最小剩余误差的面部类别对面部测试样本进行分类; 并呈现面部测试样本的面部类。

公开(公告)号：US09430697B1

公开(公告)日：2016-08-30

申请号：US14791388

申请日：2015-07-03

Applicant: TCL RESEARCH AMERICA INC.

Inventor： Michael Iliadis ,  Armin Kappeler ,  Haohong Wang

IPC: G06K9/00 ,  G06K9/66 ,  G06K9/46 ,  G06F17/30 ,  G06T7/00

CPC classification number: G06K9/00288 ,  G06F17/30244 ,  G06K9/00221 ,  G06K9/00765 ,  G06K9/4609 ,  G06K9/4671 ,  G06K9/6255 ,  H04N2201/3274

Abstract: The present invention provides a face recognition method. The method includes obtaining a plurality of training face images which belongs to a plurality of face classes and obtaining a plurality of training dictionaries corresponding to the training face images. A face class includes one or more training face images. The training dictionaries include a plurality of deep feature matrices. The method further includes obtaining an input face image. The input face image is partitioned into a plurality of blocks, whose corresponding deep feature vectors are extracted using a deep learning network. A collaborative representation model is applied to represent the deep feature vectors with the training dictionaries and representation vectors. A summation of errors for all blocks corresponding to a face class is computed as a residual error for the face class. The input face image is classified by selecting the face class that yields a minimum residual error.

Abstract translation: 本发明提供一种人脸识别方法。 该方法包括获得属于多个面部类别的多个训练面部图像，并且获得与训练面部图像对应的多个训练词典。 面部课程包括一个或多个训练面部图像。 训练词典包括多个深特征矩阵。 该方法还包括获得输入面部图像。 将输入的面部图像分割成多个块，使用深度学习网络提取其对应的深度特征向量。 应用协作表示模型来表示具有训练词典和表示向量的深特征向量。 对于面部类对应的所有块的误差的求和被计算为面部类的残差。 输入面部图像通过选择产生最小残差的面部类别进行分类。

公开(公告)号：US09430694B2

公开(公告)日：2016-08-30

申请号：US14534688

申请日：2014-11-06

Applicant: TCL RESEARCH AMERICA INC.

Inventor： Michael Iliadis ,  Haohong Wang

IPC: G06K9/36 ,  G06K9/00 ,  G06F17/30

CPC classification number: G06K9/00261 ,  G06F17/30256 ,  G06F17/30598 ,  G06K9/00288 ,  G06K9/00295 ,  G06K9/00751

Abstract: A face recognition method is provided. The method includes dividing an input video into different sets of frames and detecting faces of each frame in the input video. The method also includes generating face tracks for the whole video. Further, the method includes applying a robust collaborative representation-based classifier to recover a clean image from complex occlusions and corruptions for a face test sample and perform classification. In addition, the method also includes outputting the video containing the recognized face images.

Abstract translation: 提供一种人脸识别方法。 该方法包括将输入视频划分成不同的帧集合并且在输入视频中检测每帧的面部。 该方法还包括为整个视频生成面部轨迹。 此外，该方法包括应用强大的基于协作表示的分类器来从脸部测试样本的复杂闭塞和破坏中恢复清晰图像并执行分类。 此外，该方法还包括输出包含识别的脸部图像的视频。

公开(公告)号：US09275309B2

公开(公告)日：2016-03-01

申请号：US14449352

申请日：2014-08-01

Applicant: TCL RESEARCH AMERICA INC.

Inventor： Michael Iliadis ,  Haohong Wang

IPC: G06K9/00 ,  G06K9/66 ,  G06K9/62

CPC classification number: G06K9/66 ,  G06K9/00275 ,  G06K9/00288 ,  G06K9/6255 ,  G06K2009/4695

Abstract: A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.

Abstract translation: 提供了一种面部识别方法，用于在计算设备上使用稀疏表示和正则化最小二乘法分类。 该方法包括获得被识别为测试样本y的图像和某些被摄体的训练图像的集合作为训练样本矩阵T，获得测试样本的稀疏表示，以及包括稀疏矢量a的初始估计的训练样本 并且构建新的面部词典，其包括用于初始估计的稀疏向量a中具有非零对应系数的训练样本。 该方法还包括通过基于构造的新的面部词典求解正则化最小二乘问题来获得新系数，并且基于通过使用新系数计算的最小类残差来确定测试样本的面部身份。