WO2019037346A1 - 优化人脸图片质量评价模型的方法及装置 - Google Patents
优化人脸图片质量评价模型的方法及装置 Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 15
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- the present invention relates to the field of image analysis technology, and in particular, to a method, device, storage medium and computer device for optimizing a face picture quality evaluation model.
- the accuracy of the existing face image quality evaluation method in the actual non-limiting application scenario is much lower than the accuracy in the experiment.
- the reason is that the general face image quality evaluation method is subjective selection of face images. Different features or attributes, such as the number of pixels in a unit image, to evaluate the quality of the picture.
- the recognition accuracy of the face image that is subjective to the human eye in the face recognition algorithm is not necessarily high. Therefore, the existing face picture quality evaluation model has a poor evaluation effect on the face picture quality.
- the present invention provides a method and a device for optimizing a face picture quality evaluation model, which can improve the objectivity of a face picture quality evaluation model for image quality evaluation, and thereby better evaluate the quality of a face picture.
- the inventive solution includes:
- an embodiment of the present invention provides a method for optimizing a face image quality evaluation model, including:
- the test set includes a plurality of face images to be tested, and each face image to be tested is marked with corresponding first identity information;
- the face database includes a plurality of sample face images, and each sample face image is marked with corresponding second identity information;
- the regression neural network is used to train the initial face image quality evaluation model and parameters, and the optimized face image quality evaluation model and parameters are obtained.
- an embodiment of the present invention provides an apparatus for optimizing a face image quality evaluation model, including:
- test set establishing module configured to set a face image test set;
- the test set includes a plurality of face images to be tested, and each face image to be tested is marked with corresponding first identity information;
- An initial recognition module configured to identify a similarity between the face image to be tested and the sample face image in the preset face database, and obtain each face to be tested according to the similarity, the first identity information, and the second identity information a recognition result of the image;
- the face database includes a plurality of sample face images, and each sample face image is marked with corresponding second identity information;
- a quality score calculation module configured to determine, according to the recognition result, a quality score of each face image to be tested
- the training module is used to train the initial face image quality evaluation model and parameters by using the regression model and the corresponding quality score as training data, and obtain an optimized face image quality evaluation model and parameter.
- a computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the steps of the method.
- a computer apparatus comprising a memory, a processor, and a computer program stored on the memory and operative on the processor, the processor implementing the steps of the method when the program is executed.
- the test set includes a plurality of face images to be tested, inputting a pre-established face database; and identifying a similarity between the face image to be tested and the sample face image in the preset face database, according to
- the similarity and the identity information of each picture are obtained by the recognition result of each face image to be tested, and the recognition result of each face image to be tested is obtained; the quality score of each face image to be tested is determined to test the concentrated
- the face image and its corresponding quality score are used for neural network training of training data.
- the face image quality evaluation model and parameters obtained through training are more objectively evaluated by the face image quality evaluation model and parameters. Accurate, overcoming the influence of human subjective factors on the evaluation of picture quality.
- FIG. 1 is a schematic flowchart of a method for optimizing a face picture quality evaluation model according to an embodiment
- FIG. 2 is a schematic flowchart of a method for optimizing a face picture quality evaluation model in a specific application scenario
- FIG. 3 is a schematic structural diagram of an apparatus for optimizing a face picture quality evaluation model according to an embodiment.
- FIG. 1 is a schematic flow chart of a method for optimizing a face picture quality evaluation model according to an embodiment; As shown in FIG. 1, the method for optimizing a face picture quality evaluation model in this embodiment includes the steps of:
- a face image test set is established, where the test set includes a plurality of face images to be tested, and each face image to be tested is set with corresponding first identity information.
- the face image to be tested in the face image test set is a face image collected in a specific application scenario, and can cover multiple people. Optionally, multiple pictures per person are taken from different angles.
- the identity information of the face image refers to information that can uniquely identify the identity of the person, such as the name of the person and the identification number of the person.
- the preset face database includes face images of a plurality of individuals, and each picture is marked with corresponding second identity information. Optionally, one picture per person.
- Comparing the face image to be tested with the sample face image in the preset face database is to calculate the similarity between the two faces, and the face image and the sample to be tested can be determined according to the similarity and the preset similarity threshold. Whether the face image is the same person.
- the similarity between the face image to be tested and the sample face image in the preset face database is identified, and each face to be tested is obtained according to the similarity, the first identity information, and the second identity information.
- the specific manner of the recognition result of the picture includes: respectively calculating the similarity between the face image to be tested and the face image of each sample in the face database, and obtaining the maximum similarity value and the maximum similarity of each face image to be tested.
- Corresponding sample face image; obtaining first identity information of the face image to be tested, and acquiring second identity information of the corresponding sample face image; according to the maximum value of the similarity, the preset similarity threshold, the first The identity information and the second identity information obtain the recognition result of each face image to be tested.
- the recognition result includes a type of result, a second type of result, three types of results, and four types of results;
- a type of result is that the face image to be tested and the sample face image having the same identity information are correctly identified as the same face;
- the second type of result is the wrong face image and sample to be tested with the same identity information.
- the face image is recognized as a different face;
- the three types of results are wrong to identify the face image and the sample face image with different identity information as the same face;
- the four types of results are correct and will have different identity information.
- the face image and the sample face image are identified as different faces.
- the face recognition algorithm is preset to calculate the similarity between the face image to be tested and the face image of each sample in the face database, and obtain the maximum similarity of each face image to be tested. The value, and the corresponding sample face image when the similarity is maximum.
- a sample face image with the greatest similarity to the face image to be tested can be obtained (also referred to as a comparison sample face). Picture) and the corresponding similarity (ie the maximum similarity). And further identifying whether the face image to be tested and the face image of the comparison sample are the same face.
- the recognition result of each face image to be tested is obtained according to the maximum value of the similarity, the preset similarity threshold, and the corresponding identity information.
- the model and network parameters of the regression neural network are obtained when the preset convergence condition is met, thereby obtaining an optimized face image quality evaluation model and parameters.
- the method for optimizing the face image quality evaluation model of the above embodiment first establishes a face a picture test set; then, according to the face picture test set and the preset face database, the recognition result of each face image to be tested is obtained; and then, the quality score of each face image to be tested is determined according to the recognition result;
- the neural network training is carried out with the test face image and its corresponding face quality score in the test set as the training data, and the optimized face image quality evaluation model and parameters are obtained.
- the face image quality evaluation model and parameters can be optimized, and the optimized face image quality evaluation model and parameters are adopted.
- the evaluation of face image quality will not be affected by subjective factors, and the evaluation result is more objective and accurate.
- the face recognition algorithm separately calculates the similarity between the face image to be tested and the face image of each sample in the face database, including: separately extracting face features and samples of each face image to be tested.
- the face feature of the face image is calculated based on the extracted face features.
- the recognition result of each face image to be tested is obtained according to the maximum value of the similarity, the preset similarity threshold, and the corresponding identity information, including: the maximum value of the similarity and the pre- Comparing the similarity thresholds to obtain a first comparison result; comparing the first identity information with the second identity information to obtain a second comparison result; according to the first comparison result, the second comparison As a result, the recognition result of each face image to be tested is obtained.
- the identification result includes: a type of result, a second type of result, a third type of result, and four types of results; wherein, in the one type of result and the three types of results, the maximum value of the similarity is greater than or equal to the similarity threshold; In the second type of results and the four types of results, the maximum similarity is smaller than the similarity threshold.
- the implementation manner of obtaining the identification result is specifically as follows:
- test_name If score ⁇ threshold, and name_labeled is different from test_name, it is identified as four types of results;
- the score indicates the maximum degree of similarity corresponding to the face image to be tested
- the threshold indicates the similarity threshold
- the name_labeled indicates the identity information corresponding to the sample face image
- the test_name indicates the identity information corresponding to the face image to be tested.
- the manner in which the quality score is calculated may be:
- FIG. 2 is a schematic flow chart of a method for optimizing a face picture quality evaluation model according to a specific embodiment, which is specifically divided into four parts: data set finishing S21, face recognition S22, face quality labeling S23, and face quality.
- the method for optimizing a face image quality evaluation model in this embodiment includes the following steps:
- the data collating part includes: the setting of the face recognition algorithm face_identify_algorithm, the preset of the face database face_gallery_set, and the establishment of the face test set face_test_set.
- the similarity threshold is used to determine whether two face images belong to one person; and the preset face database includes multiple sample face images (for example, N sheets) , N is greater than 1), each sample face picture is set with corresponding second identity information; Optional, there is only one picture per person.
- the established face test set includes a plurality of face images to be tested, and each face image to be tested is set with corresponding first identity information; optionally, the face image to be tested is collected in a face image of a plurality of different angles. . It can be understood that the first identity information set by the face image to be tested and the second identity information set by the sample face image are the same type of information, for example, both are names.
- the face recognition portion determines the recognition result of the face image to be tested according to the similarity threshold threshold.
- the specific method is: extracting feature information of each test face image (assuming that the name of the current test face image is marked as test_name) by using a face recognition algorithm (the feature information includes both subjective perception features) Information, such as pixels, brightness, etc.; also includes other feature information that is not perceived by humans); the feature information of the extracted test face image is compared with the feature information corresponding to N sample face images in the face database, respectively, N similarities are obtained; the N similarity results are sorted, and the similarity score score with the largest similarity and the name_labeled of the corresponding sample face image are taken, and the following recognition result identification_result is obtained:
- FP (ie, the second type of result): if score ⁇ threshold, but name_labeled is the same as test_name;
- TN (ie four types of results): if score ⁇ threshold, and name_labeled is different from test_name.
- One type of result is the correct face image and sample face image that will have the same identity information. Recognized as the same face; the second type of result is wrong to identify the face image and the sample face image with the same identity information as different faces; the three types of results are the wrong faces with different identity information.
- the picture and the sample face picture are recognized as the same face; the four types of results are correct to identify the face image and the sample face picture with different identity information as different faces.
- the quality score of the face test picture is obtained according to the recognition result of step S22.
- the face quality annotation is to add the calculated face quality score to the face image test set to obtain a face quality test set, which is a test picture set containing the calculated face quality score.
- This process uses regression neural network training data to obtain regression neural network parameters.
- the regression neural network model can be regarded as the face image quality evaluation model to be optimized, the parameters in the regression neural network and the parameters of the face image quality evaluation model, and the neural network training is to train the face image quality.
- the evaluation model and parameters that is, the regression neural network is used to train the parameters involved in the face image quality evaluation model.
- the preset convergence condition is met, the regression neural network model and parameters are obtained, thereby obtaining optimized Face image quality evaluation model and parameters.
- face_identify_algorithm face_gallery_set, face_test_set, and threshold are used in the above technical features to represent a face recognition algorithm, a face database, a face picture test set, and a similarity threshold.
- the above representation should not be construed as a limitation on the face recognition algorithm, the face database, the face picture test set, and the similarity threshold.
- the present invention also provides an apparatus for optimizing a face picture quality evaluation model, which can be used to perform the above method for optimizing a face picture quality evaluation model.
- an apparatus for optimizing a face picture quality evaluation model which can be used to perform the above method for optimizing a face picture quality evaluation model.
- the illustrated structure does not constitute a limitation to the device, and may include more or fewer components than those illustrated, or a combination of certain components, or a different component arrangement.
- FIG. 3 is a schematic structural diagram of an apparatus for optimizing a face picture quality evaluation model according to an embodiment of the present invention.
- the apparatus for optimizing a face picture quality evaluation model in this embodiment includes: a test set establishing module 310.
- the initial identification module 320, the quality score calculation module 330, and the training module 340 are detailed as follows:
- the test set establishing module 310 is configured to establish a face image test set; the test set includes a plurality of face images to be tested, and each face image to be tested is set with corresponding first identity information;
- the initial identification module 320 is configured to identify a similarity between the face image to be tested and the sample face image in the preset face database, and obtain the respective similarities according to the similarity, the first identity information, and the second identity information. Determining a recognition result of the face image; the face database includes a plurality of sample face images, and each sample face image is marked with corresponding second identity information;
- the quality score calculation module 330 is configured to determine a quality score of each face image to be tested according to the recognition result
- the training module 340 is configured to train the initial face image quality evaluation model and parameters by using a regression neural network with the image of the face to be tested and the corresponding quality score as the training data, and obtain the optimized face image quality. Evaluate models and parameters.
- the initial recognition module 320 is configured to separately calculate the similarity between the face image to be tested and the face image of each sample in the face database. Obtaining a maximum similarity value corresponding to each face image to be tested, and a corresponding sample face image when the similarity maximum value is obtained; acquiring first identity information of the face image to be tested, and acquiring a second corresponding face image of the sample face Identity information: obtaining, according to the maximum value of the similarity, the preset similarity threshold, the first identity information, and the second identity information, a recognition result of each face image to be tested.
- the initial recognition module 320 may include a similarity calculation unit and a face recognition unit.
- the similarity calculation unit is configured to separately calculate the similarity between the face image to be tested and the face image of each sample in the face database, and obtain the maximum similarity and similarity of each face image to be tested. The corresponding sample face image at the maximum value.
- the face recognition unit is configured to obtain a recognition result of each face image to be tested according to the maximum value of the similarity, the preset similarity threshold, and the identity information corresponding to the picture.
- the similarity calculation unit may separately extract a face feature of each face image to be tested, a face feature of the sample face image, and calculate a similarity based on the extracted face feature.
- the face recognition unit is configured to compare the maximum value of the similarity with a preset similarity threshold to obtain a first comparison result; and the first identity information and The second identity information is compared to obtain a second comparison result; and the recognition result of each face image to be tested is obtained according to the first comparison result and the second comparison result.
- the recognition result of each face image to be tested is obtained as follows:
- test_name If score ⁇ threshold, and name_labeled is different from test_name, it is identified as four types of results;
- score represents the maximum similarity of the face image to be tested
- threshold represents phase
- name_labeled represents the identity information corresponding to the sample face image
- test_name represents the identity information corresponding to the face image to be tested.
- the quality score calculation module 330 is configured to extract a similarity maximum value and a similarity threshold corresponding to each recognition result; and determine, according to the recognition result, the similarity maximum value, and the similarity threshold, The quality score of each face image to be tested.
- the quality score calculation module 330 can calculate the quality score of each face image to be tested by the following formula:
- each face information corresponding to the identity information is at least two; in the face database, The sample face image corresponding to each identity information is one.
- each program module is only an example, and the actual application may be implemented according to requirements, for example, according to configuration requirements of the corresponding hardware or software.
- the above function assignment is performed by different program modules, that is, the internal structure of the device for optimizing the face picture quality evaluation model is divided into different processes.
- the module is programmed to perform all or part of the functions described above.
- the storage medium may also be disposed in a computer device, where the computer device further includes a processor, and when the processor executes the program in the storage medium, all of the embodiments of the foregoing methods can be implemented. Or part of the steps.
- the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).
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Abstract
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Claims (10)
- 一种优化人脸图片质量评价模型的方法,其特征在于,包括:建立人脸图片测试集;测试集中包括多个待测人脸图片,各待测人脸图片标注有对应的第一身份信息;识别待测人脸图片与预设人脸数据库中的样本人脸图片的相似度,根据所述相似度、第一身份信息和第二身份信息得出各待测人脸图片的识别结果;所述人脸数据库中包括多个样本人脸图片,各样本人脸图片标注有对应的第二身份信息;根据所述识别结果确定各待测人脸图片的质量分数;以各待测人脸图片及其对应的质量分数为训练数据,采用回归型神经网络对初始人脸图片质量评价模型及参数进行训练,得到优化的人脸图片质量评价模型及参数。
- 根据权利要求1所述的优化人脸图片质量评价模型的方法,其特征在于,识别待测人脸图片与预设人脸数据库中的样本人脸图片的相似度,根据所述相似度、第一身份信息和第二身份信息得出各待测人脸图片的识别结果,包括:分别计算待测人脸图片与人脸数据库中各样本人脸图片的相似度,得出各待测人脸图片对应的相似度最大值,以及相似度最大值时对应的样本人脸图片;获取待测人脸图片的第一身份信息,获取对应的样本人脸图片的第二身份信息;根据所述相似度最大值、预设的相似度阈值、第一身份信息和第二身份信息,得到各待测人脸图片的识别结果。
- 根据权利要求2所述的优化人脸图片质量评价模型的方法,其特征在于,分别计算待测人脸图片与人脸数据库中各样本人脸图片的相似度,包括:分别提取各待测人脸图片的人脸特征、样本人脸图片的人脸特征,基于提取的人脸特征计算相似度。
- 根据权利要求2或3所述的优化人脸图片质量评价模型的方法,其特征在于,根据所述相似度最大值、预设的相似度阈值、第一身份信息和第二身份信息,得到各待测人脸图片的识别结果,包括:将所述相似度最大值和预设的相似度阈值进行比对,得到第一比对结果;将所述第一身份信息和第二身份信息进行比对,得到第二比对结果;根据第一比对结果、第二比对结果得到各待测人脸图片的识别结果。
- 根据权利要求4所述的优化人脸图片质量评价模型的方法,其特征在于,所述识别结果包括:一类结果、二类结果、三类结果和四类结果;其中,一类结果和三类结果中,相似度最大值大于或等于所述相似度阈值;二类结果和四类结果中,相似度最大值小于所述相似度阈值;根据所述识别结果确定各待测人脸图片的质量分数,包括:提取各识别结果所对应的相似度最大值和相似度阈值;根据所述识别结果、相似度最大值和相似度阈值,确定各待测人脸图片的质量分数。
- 根据权利要求1、2、3、5或6所述的优化人脸图片质量评价模型的方法,其特征在于,所述测试集中,同一身份信息对应的待测人脸图片为至少两张;所述人脸数据库中,同一身份信息对应的样本人脸图片为一张。
- 一种优化人脸图片质量评价模型的装置,其特征在于,包括:测试集建立模块,用于建立人脸图片测试集;测试集中包括多个待测人脸图片,各待测人脸图片标注有对应的第一身份信息;初始识别模块,用于识别待测人脸图片与预设人脸数据库中的样本人脸图片的相似度,根据所述相似度、第一身份信息和第二身份信息得出各待测人脸图片的识别结果;所述人脸数据库中包括多个样本人脸图片,各样本人脸图片标注有对应的第二身份信息;质量分数计算模块,用于根据所述识别结果确定出各待测人脸图片的质量分数;以及,训练模块,用于以各待测人脸图片及其对应的质量分数为训练数据,采用回归型神经网络对初始人脸图片质量评价模型及参数进行训练,得到优化的人脸图片质量评价模型及参数。
- 一种计算机可读存储介质,其上存储有计算机程序,其特征在于,该程序被处理器执行时实现权利要求1至7任一所述方法的步骤。
- 一种计算机设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,其特征在于,所述处理器执行所述程序时实现权利要求1至7任一所述方法的步骤。
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