Film and television role face age reduction remodeling method and system based on deep learning

Abstract

The invention relates to the technical field of image smoothing filtering, in particular to a film and television character face age reduction remodeling method and system based on deep learning. According to the method, a wrinkle region and an eye region in a face image of a film and television role are recognized, the face image is input into a deep convolutional network, and the deep convolutional network performs feature extraction of organ expression change parameters of the eye region and feature extraction of a wrinkle appearing coefficient of the face image. And determining wrinkle appearing correlation parameters of the face image through analysis between adjacent frames. And taking the minimum wrinkle appearing coefficient as a reference frame to obtain a wrinkle appearing elimination coefficient, and adjusting a wrinkle appearing elimination parameter by using a wrinkle appearing correlation parameter to realize self-adaptive smoothing of the face image and obtain a face age reduction remodeling result. According to the method, the smooth parameters of the character face image are adjusted, so that the problems of over-fitting and unnatural age reduction and remodeling results are avoided, and the quality of the age reduction and remodeling results is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image smoothing filtering, and in particular to a method and system for reshaping the faces of film and television characters based on deep learning. Background Art

[0002] Driven by the film industry's pursuit of authenticity and diversity in character portrayal, coupled with audiences' expectations for a high-quality visual experience, and with the rapid development of computer graphics, image science, and computer hardware and software technologies, digital film production has completely replaced film production. The advent of digital technology has not only greatly enriched filmmaking methods but also provided more possibilities for character portrayal, enabling the de-aging and reshaping of film and television characters' faces. The most crucial step in this de-aging process is smoothing out facial wrinkles.

[0003] Because human faces differ in morphology and expression, the degree of wrinkle appearance varies at different times. Therefore, directly using fixed smoothing parameters to smooth wrinkles in facial images will lead to an obvious sense of inconsistency, affecting the character's age-reduction and reshaping effect. Summary of the Invention

[0004] In order to solve the technical problem that the existing technology uses fixed smoothing parameters to smooth wrinkles in facial images, which affects the effect of character de-aging and reshaping, the purpose of the present invention is to provide a method and system for de-aging the face of film and television characters based on deep learning. The technical solutions adopted are as follows:

[0005] The present invention proposes a method for reducing the age of a film and television character's face based on deep learning, the method comprising:

[0006] Obtain multiple frames of facial images of film and television characters, identify wrinkle areas and eye areas in the facial images, and identify the edge contours and center points of the eye areas; input the facial images into a deep convolutional network to obtain a facial age reduction and reshaping result; the method for processing facial images using the deep convolutional network includes:

[0007] Step S1: using the line between the center point and each edge contour point as a reference line; obtaining the organ expression change parameter of the eye region according to the discontinuity of the edge contour points and the length of the reference line;

[0008] Step S2: Extending the reference line and defining its intersection with the nearest wrinkle region as an associated wrinkle point; defining a line connecting the associated wrinkle points as an associated wrinkle line in each wrinkle region; and obtaining a wrinkle appearance coefficient for each wrinkle region based on the proportion of the associated wrinkle line in the wrinkle region and the pixel value of the wrinkle region.

[0009] Step S3: Obtaining the overall wrinkle appearance coefficient of the current facial image based on the wrinkle appearance coefficients of all wrinkle areas, and obtaining the wrinkle appearance correlation parameters of each facial image frame based on the differences in the overall wrinkle appearance coefficients and the differences in organ expression change parameters between adjacent facial images;

[0010] Step S4: Among multiple frames of facial images, select the smallest wrinkle appearance coefficient as a reference frame, filter the wrinkle area in the reference frame, and obtain wrinkle appearance elimination parameters based on the difference in wrinkle appearance coefficients of the key frames before and after filtering; for each facial image, obtain the smoothing processing coefficient of each facial image based on the wrinkle appearance elimination parameters and the wrinkle appearance associated parameters and perform smoothing processing to obtain the facial age reduction and reshaping result.

[0011] Furthermore, the method for acquiring the face image includes:

[0012] Video frames containing faces of film and television characters are obtained, the number of optical flow movements of each video frame is obtained, and video frames whose number of optical flow movements is less than a preset threshold are used as the face images.

[0013] Furthermore, the method for obtaining the eye region includes:

[0014] The eye area is obtained based on Haar-like features using a Haar classifier, and the polar coordinates of the edge contour points of the eye area are obtained. The intersection of the straight lines where the polar coordinates are located is taken as the center point.

[0015] Furthermore, the method for obtaining the organ expression change parameter includes:

[0016] The difference between the average value of the distances between adjacent edge contour points and the positive integer 1 is used as the discontinuity;

[0017] The length of the shortest reference line is normalized and then multiplied by the discontinuity to obtain the organ expression change parameter.

[0018] Furthermore, the method for obtaining the wrinkle appearance coefficient includes:

[0019] The wrinkle appearance coefficient is obtained by normalizing the ratio of the area proportion of the associated wrinkle line in the wrinkle area to the average pixel value of the wrinkle area.

[0020] Furthermore, the method for obtaining the overall wrinkle appearance coefficient includes:

[0021] The average value of the wrinkle appearance coefficients of all wrinkle areas in the face image is used as the overall wrinkle appearance coefficient.

[0022] Furthermore, the method for obtaining wrinkle appearance-related parameters includes:

[0023] The ratio of the difference in the organ expression change parameter to the difference in the overall wrinkle appearance coefficient is normalized to obtain the wrinkle appearance correlation parameter.

[0024] Furthermore, the method for obtaining wrinkle appearance and elimination parameters includes:

[0025] The wrinkle appearance elimination parameter is obtained by normalizing the difference between the wrinkle appearance coefficient of the key frame and the wrinkle appearance coefficient of the filtered image of the key frame.

[0026] Furthermore, the method for obtaining the smoothing coefficient includes:

[0027] The sum of the normalized wrinkle appearance associated parameter and the positive integer 1 is used as the adjustment magnification, and the product of the adjustment magnification and the wrinkle appearance elimination parameter is used as the smoothing coefficient.

[0028] The present invention also proposes a system for reducing the age of faces and reshaping film and television characters based on deep learning, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements any one of the steps of the method for reducing the age of faces and reshaping film and television characters based on deep learning.

[0029] The present invention has the following beneficial effects:

[0030] The present invention integrates a facial image processing algorithm into a deep convolutional network, and uses the deep convolutional network to obtain multiple image features in facial images. Considering that the eyes are the main organ that changes when the face makes an expression and produces wrinkles, the eye region is identified, and the organ expression change parameters are determined based on the morphology of the eye region contour. The present invention aims to appropriately smooth wrinkles based on facial expressions to avoid over-smoothing and under-smoothing. Therefore, it is necessary to determine the correlation between the eye region and wrinkles. Therefore, in the wrinkle region, the positional relationship between the organ and the wrinkle region is used to determine the associated wrinkle line in each wrinkle region, and then the wrinkle appearance coefficient of each wrinkle region is obtained. That is, the larger the wrinkle appearance coefficient, the more obvious the wrinkles in the facial image, and the greater the degree of smoothing required. Further considering the information changes between adjacent frames of facial images, the wrinkle appearance association parameters of each frame of facial image are effectively determined. The wrinkle elimination parameters obtained by taking the facial image with the minimum wrinkle appearance coefficient as the reference frame can be used as the basis of the smoothing parameters. Because it is the information obtained from the reference frame of the minimum wrinkle appearance coefficient, other facial images can be amplified based on it in combination with the wrinkle appearance-related parameters to obtain the smoothing processing parameters of each facial image and perform effective facial de-aging and reshaping. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A flowchart of a method for processing facial images using a deep convolutional network in a method for reducing the age of film and television character faces based on deep learning provided by one embodiment of the present invention;

[0033] Figure 2 A schematic diagram of wrinkle area identification provided by one embodiment of the present invention;

[0034] Figure 3 A schematic diagram of an eye region outline provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0035] To further illustrate the technical means and effectiveness of the present invention to achieve the intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for reshaping the face of a film or television character based on deep learning, including its specific implementation, structure, features, and effectiveness. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0037] The present invention aims to process facial images using a deep convolutional network, extracting features from the facial image and performing operations such as convolution and pooling, ultimately determining the facial de-aging and reshaping results. The deep neural network used in the present invention is the AlexNet network structure, which is a well-known technical means for those skilled in the art and will not be described in detail here. The following briefly describes the characteristics of the network structure and the data processing method:

[0038] The AlexNet network structure consists of 5 convolutional layers and 3 fully connected layers. Each convolutional layer contains a convolution kernel, a bias term, a ReLU activation function, and a local response normalization (LRN) module. The 1st, 2nd, and 5th convolutional layers are followed by a maximum pooling layer.

[0039] After the facial images of film and television characters are input into the network model, each convolution layer uses a filter based on the image to slide once in the wide dimension or high dimension of the image. At this time, the filter and the input image form a corresponding relationship in space, and the inner product operation is performed at the corresponding position in this space to obtain a pixel value in the output feature map. Then, multiple slides are performed until the complete feature map is obtained. When all filters have completed the feature extraction of the image, the feature maps are superimposed in the depth dimension to form the output of the convolution layer. Among them, the wrinkle appearance coefficient and the organ expression change parameter obtained later in the embodiment of the present invention are the weights during the image convolution processing. The spatial dimension of the feature map is reduced in the pooling layer through the weights, and the feature information can be retained. After the feature extraction of each facial image, the facial images of adjacent frames can be analyzed to determine the wrinkle appearance correlation parameters, and then the wrinkle appearance elimination parameters are used to determine the smoothing processing coefficient of the final facial image, and smoothing is performed to output the facial age reduction and reshaping result.

[0040] The embodiment of the present invention should also go through a series of preprocessing processes before the face image is input into the deep convolutional network to facilitate feature extraction in the network. First, the face image of the film and television character should be determined in the film and television video, and then the wrinkle area and eye area in the face image should be identified. The wrinkle area is the area that needs to be smoothed by the embodiment of the present invention, because the main wrinkles on the face, such as forehead wrinkles, crow's feet, etc., include static wrinkles and dynamic wrinkles, among which the characteristics of static wrinkles are relatively fixed, while dynamic wrinkles are affected by facial expressions. The eyes are important facial organs, and facial expressions that produce wrinkles are often accompanied by changes in the eyes. For example, when a person smiles, his eyes will squint, which will cause obvious wrinkles at the corners of the eyes. Therefore, the embodiment of the present invention identifies the eye area to facilitate the processing of facial images in subsequent steps in the deep convolutional network.

[0041] Preferably, in one embodiment of the present invention, considering that the facial expressions of characters in film and television videos are continuous, there are useless image frames. Such image frames change frequently and rapidly, and because the facial features are weak, there is no need to perform facial de-aging. Therefore, the embodiment of the present invention obtains video frames containing the faces of film and television characters based on motion analysis, obtains the number of optical flow movements for each video frame, and uses the video frames whose optical flow movement number is less than a preset threshold as the facial image. It should be noted that the threshold setting for the number of optical flow movements can be set according to the specific content of the film and television video. In the embodiment of the present invention, the optical flow movement numbers of all video frames containing the faces of film and television characters are arranged from small to large, and the optical flow movement numbers in the top 30% are selected as the threshold.

[0042] In the embodiment of the present invention, the wrinkle region extraction method can be based on the content of "Research on Wrinkle Detection and Quantitative Evaluation of Facial Images", combining the filtering results of Gabor filter bank and Frangi filter as image features, and extracting features of wrinkles of different coarseness and fineness. Figure 2 , which shows a schematic diagram of wrinkle area identification provided by an embodiment of the present invention.

[0043] Preferably, in an embodiment of the present invention, the face image is regarded as a whole two-dimensional pixel matrix, and a face pattern space is constructed from a statistical point of view through a large number of face image samples, and then the Haar classifier is used to obtain the eye area based on Haar-like features, and the polar coordinate points of the edge contour points of the eye area are obtained, and the intersection of the straight lines where the polar coordinate points are located is used as the center point. It should be noted that the Haar classifier will eventually obtain multiple strange faces, because the eye area has unique characteristics compared to other organs. For example, compared with the bridge of the nose, the pixel points of the eye part are smaller, and the pixel values of the bridge of the nose part are higher. In addition, there are different pixel value distributions of the pupil, white of the eye, and upper and lower eyelids inside the eye. The eye area can be extracted separately through contour detection and threshold segmentation technology. The specific means are technical means well known to those skilled in the art and will not be described here. Please refer to Figure 3 , which shows a schematic diagram of an eye region outline provided by an embodiment of the present invention, Figure 3 The origin O in the upper left corner is the origin of the image coordinate system. The two coordinate axes of the image coordinate system are i and j. By counting the polar coordinates of all edge contour points, two vertical extreme lines can be obtained. The center pixel point corresponding to the extreme line is recorded as the center point.

[0044] See also Figure 1 , which shows a flowchart of a method for processing facial images using a deep convolutional network in a method for reducing the age of film and television character faces based on deep learning provided by one embodiment of the present invention. The method includes:

[0045] Step S1: taking the line between the center point and each edge contour point as a reference line; and obtaining the organ expression change parameter of the eye region according to the discontinuity of the edge contour points and the length of the reference line.

[0046] The eye area is an area with obvious facial features, and its contour features have a clear correlation with the appearance of wrinkles. For example, when a character smiles, his eyes will squint, and there will be a slight upward change in the corners of the eyes. At this time, the distance between the top and bottom of the eyes and the center point is small, and obvious wrinkles will appear at the corners of the eyes; for example, when a character raises his eyebrows, his eyes will open wide, and the distance between the top and bottom of the eyes and the center point is small, and the eye contour changes significantly, and forehead wrinkles will appear on the character's forehead. Therefore, the embodiment of the present invention connects the center point with each edge contour point to obtain a reference line, that is, an eye area contains multiple reference lines. Further considering the discontinuity of the eye area, the greater the discontinuity, the greater the degree of deformation of the eye contour, that is, the greater the degree of expression change at this time. Therefore, the organ expression change parameter of the eye area can be obtained based on the discontinuity of the edge contour point and the length of the reference line. The organ expression change parameter characterizes the expression change characteristics of the eye area in the current face image.

[0047] Preferably, in one embodiment of the present invention, the method for acquiring organ expression change parameters includes:

[0048] The difference between the average distance between adjacent edge contour points and the positive integer 1 is used as the discontinuity. In other words, the present invention assumes that, ideally, the distance between adjacent edge contour points should be 1, resulting in a calm expression and no noticeable change in the eye contour. Therefore, the greater the difference between the average distance between adjacent edge contour points and the positive integer 1, the more pronounced the eye contour changes, and the more pronounced the eye's organ expression change parameter should be.

[0049] The length of the shortest reference line is normalized and then multiplied by the discontinuity to obtain the organ expression change parameter. In the embodiment of the present invention, the length of the shortest reference line is used as the expression feature of the eye at that time. The longer the length of the shortest reference line is, the greater the degree of opening and closing of the eyes, and the greater the organ expression change parameter. It should be noted that the method for normalizing the length of the shortest reference line in the embodiment of the present invention is: using the length of the shortest reference line as the numerator and the length of the average reference line as the denominator to obtain the normalized result.

[0050] In one embodiment of the present invention, in order to facilitate subsequent data processing, normalization processing needs to be performed after obtaining the organ expression change parameters, and a normalization result with a value range between 0 and 1 is obtained using a softsign function.

[0051] Step S2: Extend the reference line and use the intersection with the nearest wrinkle area as the associated wrinkle point; in each wrinkle area, use the line connecting the associated wrinkle points as the associated wrinkle line; and obtain the wrinkle appearance coefficient of each wrinkle area based on the proportion of the associated wrinkle line in the wrinkle area and the pixel value of the wrinkle area.

[0052] Wrinkles on a character's face can be classified into two types: static and dynamic. Dynamic wrinkles arise from changes in facial expression. Therefore, to eliminate dynamic wrinkles using appropriate smoothing parameters, it's necessary to determine the correlation between the eye and wrinkle regions. Therefore, based on the relationship between the eye and wrinkle locations, this embodiment of the present invention extends a reference line and uses its intersection with the nearest wrinkle region as the associated wrinkle point. The line connecting the associated wrinkle points is then called the associated wrinkle line.

[0053] The greater the proportion of associated wrinkle lines in the wrinkle area, the more obvious the dynamic wrinkles are. At the same time, the darker the pixel value in the wrinkle area, the more obvious the wrinkles are relative to normal skin. Therefore, the wrinkle appearance coefficient of each wrinkle area can be obtained based on the proportion of associated wrinkle lines in the wrinkle area and the pixel value of the wrinkle area.

[0054] Preferably, in an embodiment of the present invention, the method for obtaining the wrinkle appearance coefficient includes:

[0055] The wrinkle appearance coefficient is obtained by normalizing the ratio of the area ratio of the associated wrinkle line in the wrinkle region to the average pixel value of the wrinkle region. It should be noted that the area ratio in this embodiment of the present invention is the ratio of the number of pixels on the associated wrinkle line to the number of pixels within the minimum bounding rectangle of the wrinkle region.

[0056] Step S3: Obtain the overall wrinkle appearance coefficient of the current face image based on the wrinkle appearance coefficients of all wrinkle areas, and obtain the wrinkle appearance correlation parameters of each face frame based on the differences in the overall wrinkle appearance coefficients between adjacent frames of face images and the differences in organ expression change parameters.

[0057] After processing in step S1, each wrinkle region in the facial image corresponds to a wrinkle appearance coefficient. Therefore, all wrinkle appearance coefficients can be calculated to obtain the overall wrinkle appearance coefficient for the current facial image. The overall wrinkle appearance coefficient reflects the degree of wrinkle appearance in the current facial image, particularly the degree of wrinkle appearance caused by facial expression. Because facial expressions are dynamic, further analysis can be performed to determine the differences in overall wrinkle appearance coefficients between facial images at adjacent moments, as well as differences in organ expression change parameters. This information can reveal the correlation between wrinkle appearance coefficients after facial expression changes between adjacent frames, thereby obtaining the wrinkle appearance correlation parameter for each facial image frame. A large wrinkle appearance correlation parameter indicates that the wrinkle region in that facial image frame is likely caused by drastic or frequent changes in facial expression, exhibiting more pronounced wrinkle characteristics and requiring a larger smoothing parameter.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining wrinkle appearance-related parameters includes:

[0059] The ratio of the difference in organ expression change parameters to the difference in the overall wrinkle appearance coefficient is normalized to obtain the wrinkle appearance correlation parameter. In this embodiment of the present invention, for each facial image frame, the difference between the organ expression change parameters of the previous frame and the next frame is used as the organ expression change parameter difference; the overall wrinkle appearance coefficient difference is obtained in a similar manner. Specifically, the greater the difference in facial expression change between the previous and next frames, while the difference in the overall wrinkle appearance coefficient is smaller, indicating a greater correlation between the facial expression change and the wrinkle state, and the need to further emphasize the weighting features in the dynamic wrinkle smoothing process.

[0060] Step S4: Among multiple frames of facial images, select the smallest wrinkle appearance coefficient as the reference frame, filter the wrinkle area in the reference frame, and obtain the wrinkle appearance elimination parameters based on the difference in wrinkle appearance coefficients of the key frames before and after filtering; for each facial image, obtain the smoothing processing coefficient of each facial image based on the wrinkle appearance elimination parameters and the wrinkle appearance related parameters and perform smoothing processing to obtain the facial age reduction and reshaping result.

[0061] When reshaping the faces of film and television characters to reduce their age, over-smoothing of wrinkles should be avoided as much as possible. Over-smoothing wrinkles will cause the face to lose its natural feel, so some dynamic lines should be retained as much as possible to avoid over-smoothing. Therefore, it is necessary to determine a suitable smoothing parameter as basic data. Adjusting the smoothing parameter based on this basic data can prevent over-smoothing while also achieving an adaptive effect on wrinkle smoothing. Because the wrinkle appearance coefficient can reflect the degree of wrinkle visibility and the degree of dynamic line appearance, the embodiment of the present invention selects the smallest wrinkle appearance coefficient as the reference frame in multiple frames of facial images, filters the wrinkle area in the reference frame, and obtains the wrinkle appearance elimination parameter based on the difference in the wrinkle appearance coefficients of the key frames before and after filtering. That is, the wrinkle appearance elimination parameter is used as the basic smoothing parameter, and the smoothing parameters of other facial images can be adjusted and changed based on this to achieve adaptive smoothing while avoiding over-smoothing.

[0062] The embodiment of the present invention further uses wrinkle appearance associated parameters as weights, and adjusts the wrinkle appearance elimination parameters to obtain a smoothing coefficient for each facial image. After smoothing the facial image using the smoothing coefficient, a facial age reduction and reshaping result can be obtained.

[0063] Preferably, in an embodiment of the present invention, the method for obtaining wrinkle appearance and elimination parameters includes:

[0064] The difference between the wrinkle appearance coefficient of the key frame and the wrinkle appearance coefficient of the filtered image of the key frame is normalized to obtain the wrinkle appearance elimination parameter. In this embodiment of the present invention, the ratio of the difference to the wrinkle appearance coefficient of the key frame is used as the final normalization result.

[0065] Preferably, in an embodiment of the present invention, the method for obtaining the smoothing coefficient includes:

[0066] The adjustment factor is the sum of the normalized wrinkle appearance parameter and the positive integer 1. That is, the adjustment factor has a value range of 1 to 2. The product of the adjustment factor and the wrinkle appearance elimination parameter is used as the smoothing coefficient.

[0067] It should be noted that the method for filtering and smoothing wrinkles in the embodiment of the present invention may adopt an existing filtering method, such as a non-mean filtering algorithm, and the specific method is not limited thereto.

[0068] In summary, the embodiment of the present invention identifies wrinkle areas and eye areas in the facial images of film and television characters, inputs the facial images into a deep convolutional network, and the deep convolutional network performs feature extraction of the organ expression change parameters of the eye area, as well as feature extraction of the wrinkle appearance coefficient of the facial image. By analyzing adjacent frames, the wrinkle appearance associated parameters of the facial image are determined. The minimum wrinkle appearance coefficient is used as a reference frame to obtain the wrinkle appearance elimination coefficient, and the wrinkle appearance elimination parameters are adjusted using the wrinkle appearance associated parameters to achieve adaptive smoothing of the facial image and obtain a facial age reduction and reshaping result. The present invention avoids the problems of overfitting and unnatural age reduction and reshaping results by adjusting the smoothing parameters of the character's facial image, thereby improving the quality of the age reduction and reshaping results.

[0069] Based on the same inventive concept, the present invention also proposes a deep learning-based facial de-aging and reshaping system for film and television characters, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, it implements any one of the steps of a deep learning-based facial de-aging and reshaping method for film and television characters.

[0070] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for reducing the age of film and television character faces based on deep learning, characterized in that: The method comprises: Obtain multiple frames of facial images of film and television characters, identify wrinkle areas and eye areas in the facial images, and identify the edge contours and center points of the eye areas; input the facial images into a deep convolutional network to obtain a facial age reduction and reshaping result; the method for processing facial images using the deep convolutional network includes: Step S1: using the line between the center point and each edge contour point as a reference line; obtaining the organ expression change parameter of the eye region according to the discontinuity of the edge contour points and the length of the reference line; Step S2: Extending the reference line and defining its intersection with the nearest wrinkle region as an associated wrinkle point; defining a line connecting the associated wrinkle points as an associated wrinkle line in each wrinkle region; and obtaining a wrinkle appearance coefficient for each wrinkle region based on the proportion of the associated wrinkle line in the wrinkle region and the pixel value of the wrinkle region. Step S3: Obtaining the overall wrinkle appearance coefficient of the current face image based on the wrinkle appearance coefficients of all wrinkle areas, and obtaining the wrinkle appearance correlation parameter of each face image frame based on the difference in the overall wrinkle appearance coefficients and the difference in organ expression change parameters between adjacent face images; Step S4: Among multiple frames of facial images, select the smallest wrinkle appearance coefficient as a reference frame, filter the wrinkle area in the reference frame, and obtain wrinkle appearance elimination parameters based on the difference in wrinkle appearance coefficients of the key frames before and after filtering; for each facial image, obtain the smoothing processing coefficient of each facial image based on the wrinkle appearance elimination parameters and the wrinkle appearance associated parameters and perform smoothing processing to obtain the facial age reduction and reshaping result.

2. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for acquiring the face image includes: Video frames containing faces of film and television characters are obtained, the number of optical flow movements of each video frame is obtained, and video frames whose number of optical flow movements is less than a preset threshold are used as the face images.

3. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining the eye region includes: The eye area is obtained based on Haar-like features using a Haar classifier, and the polar coordinates of the edge contour points of the eye area are obtained. The intersection of the straight lines where the polar coordinates are located is taken as the center point.

4. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining the organ expression change parameter includes: The difference between the average value of the distances between adjacent edge contour points and the positive integer 1 is used as the discontinuity; The length of the shortest reference line is normalized and then multiplied by the discontinuity to obtain the organ expression change parameter.

5. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining the wrinkle appearance coefficient includes: The wrinkle appearance coefficient is obtained by normalizing the ratio of the area proportion of the associated wrinkle line in the wrinkle area to the average pixel value of the wrinkle area.

6. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining the overall wrinkle appearance coefficient includes: The average value of the wrinkle appearance coefficients of all wrinkle areas in the face image is used as the overall wrinkle appearance coefficient.

7. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining wrinkle appearance related parameters includes: The ratio of the difference in the organ expression change parameter to the difference in the overall wrinkle appearance coefficient is normalized to obtain the wrinkle appearance correlation parameter.

8. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining wrinkle appearance and elimination parameters includes: The wrinkle appearance elimination parameter is obtained by normalizing the difference between the wrinkle appearance coefficient of the key frame and the wrinkle appearance coefficient of the filtered image of the key frame.

9. The method for reducing the age of a film and television character's face based on deep learning according to claim 1, characterized in that: The method for obtaining the smoothing coefficient includes: The sum of the normalized wrinkle appearance associated parameter and the positive integer 1 is used as the adjustment magnification, and the product of the adjustment magnification and the wrinkle appearance elimination parameter is used as the smoothing coefficient.

10. A deep learning-based system for reshaping the face of a film or television character to reduce age, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for reducing the age of the face of a film and television character based on deep learning as described in any one of claims 1 to 9 are implemented.

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