CN112487904A - Video image processing method and system based on big data analysis - Google Patents
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Abstract
The invention relates to a video image processing method based on big data analysis, which comprises the following steps: establishing a face recognition database and a face micro-expression database; acquiring a video image to acquire a face image; preprocessing the collected video image to eliminate background variegation in the video image; and performing emotion analysis on the emotion characteristics in the preprocessed video image to finish the identification of the video image. The invention has the advantages that: the redundant confounding color in the image is removed by preprocessing the video image such as edge extraction, histogram equalization, skin color segmentation and illumination compensation, so that the subsequent analysis and recognition of details such as facial expression and the like are more accurate, and the recognition time is further shortened.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a video image processing method and system based on big data analysis.
Background
The face recognition is a biological recognition technology for performing identity recognition based on face feature information of a person, and is a series of related technologies, generally called face recognition or face recognition, in which a camera or a camera is used to collect an image or a video stream containing a face, and automatically detect and track the face in the image, thereby recognizing the detected face.
Most of the existing face recognition only recognizes the general features of the face, so that a person can be judged from a video image, or whether the information of the person and a database can be successfully matched or not is judged; however, in some situations, it is necessary to recognize fine expressions of faces in a video image, and therefore, it is necessary to analyze detailed portions in the video image, but too many background colors in the video cause great trouble to the detailed analysis of the image, which results in a slow video image recognition speed and even a reduction in the overall recognition accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a video image processing method and a video image processing system based on big data analysis, and overcomes the defects of the existing face recognition method.
The purpose of the invention is realized by the following technical scheme: a video image processing method based on big data analysis, the video image processing method comprising:
establishing a face recognition database and a face micro-expression database;
acquiring a video image to acquire a face image;
preprocessing the collected video image to eliminate background variegation in the video image;
and performing emotion analysis on the emotion characteristics in the preprocessed video image to finish the identification of the video image.
Further, the pre-processing the acquired video image to eliminate the background mottle in the video image comprises:
capturing the outline of a face image in an acquired video image, separating a skin color area from a background image, intercepting the captured face image from the background of the video image, and then carrying out binarization processing;
extracting the edge of the image after binarization processing, and removing an image area with weak edge and a background area with flat change to equalize the distribution of pixel values in the image;
and removing the binarization processing effect of the image after pixel value distribution equalization, and performing illumination compensation to overcome the interference of uneven brightness on the result.
Further, the equalizing the distribution of pixel values in the image specifically includes the following:
performing histogram equalization on an input image, transforming the input image to a frequency domain by using 2D-FFT, and solving the correlation between the input image and an average face template by using an optimal adaptive correlator;
dividing the output of the filter into three parts of a face region, a possible face region and a background region according to a threshold value, carrying out local gray level equalization on an image to be detected in a window of 3 multiplied by 3, and finally outputting the background region by using an OAC filter.
Further, the performing emotion analysis on the emotion features in the preprocessed video image and completing the identification of the video image includes:
analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture according to a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in a database, generating a human face model in combination, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
and (4) calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
A video image processing system based on big data analysis comprises a database construction module, an image preprocessing module and a human face emotion analysis module;
the database construction module is used for establishing a face recognition database and a face micro-expression database;
the image preprocessing module is used for preprocessing the acquired video image to eliminate background variegates in the video image;
the face emotion analysis module is used for carrying out emotion analysis on emotion characteristics in the preprocessed video image to finish the identification of the video image.
Furthermore, the image preprocessing module comprises a human face contour intercepting and processing unit, an edge extraction and equalization processing unit and an illumination compensation unit;
the human face contour screenshot and processing unit is used for capturing the contour of a human face image in an acquired video image, separating a skin color area from a background image, intercepting the captured human face image from the background of the video image and then carrying out binarization processing;
the edge extraction and equalization processing unit is used for extracting the edge of the image after binarization processing, and equalizing the distribution of pixel values in the image after removing the image area with weak edge and the background area with flat change;
the illumination compensation unit is used for removing the binarization processing effect of the image after pixel value distribution equalization and carrying out illumination compensation to overcome the interference of uneven brightness on the result.
Furthermore, the human face emotion analysis module comprises a color analysis and pixel statistics unit, a characteristic point analysis and modeling unit and an emotion analysis unit;
the color analysis and pixel statistics unit is used for analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture of a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
the characteristic point analysis and modeling unit is used for analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in the database, generating a human face model in a combining manner, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
the emotion analysis unit is used for calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
The invention has the following advantages: the video image processing method and the video image processing system based on big data analysis remove redundant confounding colors in the image by preprocessing the video image such as edge extraction, histogram equalization, skin color segmentation and illumination compensation, so that the subsequent analysis and identification of details such as facial expressions and the like are more accurate, and the identification time is further shortened.
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FIG. 1 is a flow chart of the present invention; .
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention relates to a video image processing method based on big data analysis, the video image processing method includes:
s1, establishing a face recognition database and a face micro-expression database;
s2, acquiring a video image to obtain a face image;
s3, preprocessing the collected video image to eliminate background variegated colors in the video image;
and S4, performing emotion analysis on the emotion characteristics in the preprocessed video image, and completing identification of the video image.
Further, the pre-processing the acquired video image to eliminate the background mottle in the video image comprises:
capturing the outline of a face image in an acquired video image, separating a skin color area from a background image, intercepting the captured face image from the background of the video image, and then carrying out binarization processing;
extracting the edge of the image after binarization processing, and removing an image area with weak edge and a background area with flat change to equalize the distribution of pixel values in the image;
and removing the binarization processing effect of the image after pixel value distribution equalization, and performing illumination compensation to overcome the interference of uneven brightness on the result.
Further, the equalizing the distribution of pixel values in the image specifically includes the following:
performing histogram equalization on an input image, transforming the input image to a frequency domain by using 2D-FFT, and solving the correlation between the input image and an average face template by using an optimal adaptive correlator;
dividing the output of the filter into three parts of a face region, a possible face region and a background region according to a threshold value, carrying out local gray level equalization on an image to be detected in a window of 3 multiplied by 3, and finally outputting the background region by using an OAC filter.
Further, the performing emotion analysis on the emotion features in the preprocessed video image and completing the identification of the video image includes:
analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture according to a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in a database, generating a human face model in combination, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
and (4) calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
The invention relates to a video image processing system based on big data analysis, which comprises a database construction module, an image preprocessing module and a human face emotion analysis module;
the database construction module is used for establishing a face recognition database and a face micro-expression database;
the image preprocessing module is used for preprocessing the acquired video image to eliminate background variegates in the video image;
the face emotion analysis module is used for carrying out emotion analysis on emotion characteristics in the preprocessed video image to finish the identification of the video image.
Furthermore, the image preprocessing module comprises a human face contour intercepting and processing unit, an edge extraction and equalization processing unit and an illumination compensation unit;
the human face contour screenshot and processing unit is used for capturing the contour of a human face image in an acquired video image, separating a skin color area from a background image, intercepting the captured human face image from the background of the video image and then carrying out binarization processing;
the edge extraction and equalization processing unit is used for extracting the edge of the image after binarization processing, and equalizing the distribution of pixel values in the image after removing the image area with weak edge and the background area with flat change;
the illumination compensation unit is used for removing the binarization processing effect of the image after pixel value distribution equalization and carrying out illumination compensation to overcome the interference of uneven brightness on the result.
Furthermore, the human face emotion analysis module comprises a color analysis and pixel statistics unit, a characteristic point analysis and modeling unit and an emotion analysis unit;
the color analysis and pixel statistics unit is used for analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture of a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
the characteristic point analysis and modeling unit is used for analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in the database, generating a human face model in a combining manner, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
the emotion analysis unit is used for calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. A video image processing method based on big data analysis is characterized in that: the video image processing method comprises the following steps:
establishing a face recognition database and a face micro-expression database;
acquiring a video image to acquire a face image;
preprocessing the collected video image to eliminate background variegation in the video image;
and performing emotion analysis on the emotion characteristics in the preprocessed video image to finish the identification of the video image.
2. The video image processing method based on big data analysis according to claim 1, wherein: the pre-processing of the acquired video image to eliminate background mottle in the video image comprises:
capturing the outline of a face image in an acquired video image, separating a skin color area from a background image, intercepting the captured face image from the background of the video image, and then carrying out binarization processing;
extracting the edge of the image after binarization processing, and removing an image area with weak edge and a background area with flat change to equalize the distribution of pixel values in the image;
and removing the binarization processing effect of the image after pixel value distribution equalization, and performing illumination compensation to overcome the interference of uneven brightness on the result.
3. The video image processing method based on big data analysis according to claim 2, wherein: the equalizing the distribution of pixel values in the image specifically includes the following:
performing histogram equalization on an input image, transforming the input image to a frequency domain by using 2D-FFT, and solving the correlation between the input image and an average face template by using an optimal adaptive correlator;
dividing the output of the filter into three parts of a face region, a possible face region and a background region according to a threshold value, carrying out local gray level equalization on an image to be detected in a window of 3 multiplied by 3, and finally outputting the background region by using an OAC filter.
4. The video image processing method based on big data analysis according to claim 2, wherein: the emotion feature in the preprocessed video image is subjected to emotion analysis, and the identification of the video image is completed by the emotion feature analysis method comprises the following steps:
analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture according to a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in a database, generating a human face model in combination, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
and (4) calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
5. A video image processing system based on big data analysis, characterized by: the system comprises a database construction module, an image preprocessing module and a human face emotion analysis module;
the database construction module is used for establishing a face recognition database and a face micro-expression database;
the image preprocessing module is used for preprocessing the acquired video image to eliminate background variegates in the video image;
the face emotion analysis module is used for carrying out emotion analysis on emotion characteristics in the preprocessed video image to finish the identification of the video image.
6. The video image processing system based on big data analysis according to claim 5, wherein: the image preprocessing module comprises a human face contour intercepting and processing unit, an edge extracting and equalizing processing unit and an illumination compensation unit;
the human face contour screenshot and processing unit is used for capturing the contour of a human face image in an acquired video image, separating a skin color area from a background image, intercepting the captured human face image from the background of the video image and then carrying out binarization processing;
the edge extraction and equalization processing unit is used for extracting the edge of the image after binarization processing, and equalizing the distribution of pixel values in the image after removing the image area with weak edge and the background area with flat change;
the illumination compensation unit is used for removing the binarization processing effect of the image after pixel value distribution equalization and carrying out illumination compensation to overcome the interference of uneven brightness on the result.
7. The video image processing system based on big data analysis according to claim 5, wherein: the human face emotion analysis module comprises a color analysis and pixel statistics unit, a characteristic point analysis and modeling unit and an emotion analysis unit;
the color analysis and pixel statistics unit is used for analyzing color parameters of the preprocessed image, judging the range of the face according to the color change of the image, adjusting a screenshot picture of a feedback result of the face position, pixelating the face position image according to the face position information, and performing pixel statistics face recognition on image pixels by combining face characteristic data of a database;
the characteristic point analysis and modeling unit is used for analyzing and defining human face characteristic points according to the analyzed human face result and combining micro expression characteristics in the database, generating a human face model in a combining manner, calibrating the characteristic points on the model, analyzing and adjusting the positions of the characteristic points in real time and recording the change displacement of the positions of the characteristic points;
the emotion analysis unit is used for calling micro-expression characteristics and psychological behavior characteristics in the database to analyze the emotion and emotion changes of the character according to the position change displacement of the characteristic points on the face model, and outputting an analysis result.
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