Face feature recognition system based on digital image processing

Abstract

The invention discloses a face feature recognition system based on digital image processing, which relates to the technical field of feature recognition and comprises a defect recognition end, an image retouching early warning end and an anomaly correction end. The flaw recognition end is used for determining the face flaw in real time during multi-region recognition; the image retouching early warning end is used for adjusting the facial proportion of the five sense organs of the image in real time through the standard proportion of the five sense organs and judging the abnormality of the proportion adjustment of the five sense organs in real time in multiple directions, multiple angles and multiple positions; and the abnormity correction end is used for performing all multi-direction, multi-angle and multi-position real-time calculation on a revised image correction value in real time. According to the face feature recognition system based on digital image processing, the situation that spots and flaws on the face cannot be effectively recognized during face recognition of twins is avoided, whether image retouching is abnormal or not can be judged in real time according to the adjustment proportion after image retouching, local deviation of retouching after face feature recognition is avoided, and the recognition accuracy is improved. And the problem that the overall facial features are influenced by deviation caused by manual image retouching is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of feature recognition, and in particular to a face feature recognition system based on digital image processing. Background Art

[0002] Facial feature recognition based on digital image processing is a technology that uses digital image processing technology to analyze and identify feature information in facial images. It converts color images into grayscale images, reduces the amount of data while highlighting key information such as image contours and textures for easy subsequent processing. By adjusting image contrast, brightness and other parameters, the facial features in the image are made more obvious, facilitating subsequent feature extraction.

[0003] Publication No. CN112651301A discloses an expression recognition method that integrates global and local features of the face. In the model feature extraction stage, three branches are designed; branch one uses the entire face image to extract global features of the face; branch two cuts the face into two parts from top to bottom, and branch three cuts the face into three parts from top to bottom to extract local features of the face; the extracted global and local features are fused and input into the softmax classifier for facial expression classification, and a recognition model that integrates global and local features of the face is obtained. Experiments show that the method of extracting global and local features separately and then fusing them can improve the accuracy of facial expression recognition.

[0004] After searching the above patents, it was found that there are still some deficiencies in facial feature recognition based on digital image processing: 1. When performing facial feature recognition, spots and blemishes on the face cannot be accurately recognized, which makes it easy to ignore spots and blemishes on the face when recognizing the faces of twins, resulting in deviations in facial feature recognition and errors in facial feature recognition; 2. When the image obtained by facial feature recognition is retouched, it is impossible to judge whether the retouching is abnormal in real time based on the adjusted ratio after retouching, which makes it easy for local deviations in retouching to occur after facial feature recognition. In case of local deviation, it is impossible to remind in time, affecting the effect of image processing; 3. When recognizing facial features, the abnormal retouching that occurs is usually based on manual observation of whether the retouching is deviated, and it is impossible to make an overall correction to the face based on local deviations, which greatly reduces the efficiency of image processing and causes high complexity in manual retouching.

[0005] Therefore, a face feature recognition system based on digital image processing is proposed to solve the above problems. Summary of the invention

[0006] The main purpose of the present invention is to provide a facial feature recognition system based on digital image processing to solve the problems raised in the above background.

[0007] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a facial feature recognition system based on digital image processing, including a defect recognition end, a photo editing warning end and an abnormality correction end; The defect recognition end is used to perform multi-region recognition of facial features of a person's face in real time by switching between partial scanning and full scanning of the face at will, determine the positions of the facial features of the person's face, and determine facial defects of the person's face in real time during multi-region recognition; The image editing warning terminal is used to automatically generate a face image of a human face based on the recognition result of the facial features of the human face, adjust the facial features of the image in real time according to the standard facial features ratio, and judge in real time whether the adjustment of local features affects all features, judge abnormalities of the facial features ratio adjustment in real time from multiple directions, angles and positions, and give a real-time image editing deviation warning reminder when it is judged that the local adjustment of the facial features ratio affects all features; The abnormality correction end is used to receive the local photo editing abnormality judgment results of the image processing in real time through the data receiver, and to perform real-time calculation of the photo editing correction values ​​in multiple directions, multiple angles and multiple positions when local deviations occur in the photo editing, so that all correction adjustments can be made in real time when local deviations occur in the image processing, and the photo editing evaluation can be performed in real time after the image is corrected.

[0008] The defect recognition terminal includes a local switching module, a multi-effect recognition module and a recognition classification module; The local switching module includes a local scanning unit, a full scanning unit and a facial features recognition and positioning unit; The local scanning unit is used to collect local features of the human face in real time through a 3D facial scanner, the local features including eyeball color, eyeball texture, mouth color, mouth thickness, eyebrow shape, eyebrow length, eyebrow width, cheek color, cheek shape, philtrum length, philtrum width, spots, scars, acne marks, spot color, scar size, scar color, acne mark size, acne mark color, ear color and ear shape, and record the local features of the human face in real time through a data recorder; The full scanning unit is used to collect all features of the human face in real time through a 3D facial scanner, including nose, eyes, mouth, eyebrows, cheeks, philtrum and ears, and record all features of the human face in real time through a data recorder; The facial features recognition and positioning unit is used to set the facial features model of the human face, determine the real-time positions of the facial features of the human face in real time according to the scanning results of the 3D scanner, and display the facial features in real time through a local display page.

[0009] The multi-effect recognition module is used to determine the facial blemishes of the human face in real time during multi-region recognition. Facial blemishes include spots, spot colors, scars and acne marks. The color features of the human face are calculated in real time, and the average value of the facial blemishes is calculated. , the average value of saturation and the average value of brightness As the color feature of the facial blemish, the calculation formula is as follows: ; ; ; Among them, H represents hue, S represents saturation, and V represents brightness, which represents the color characteristic component of facial blemishes. , and They are The hue, saturation, and lightness values ​​of each pixel; Set the standard color features of facial features and calculate the difference between the color of facial blemishes and the standard color features. The calculation formula is as follows: ; Where D represents the difference between the color of facial blemishes and the standard color feature. , and They are the average values ​​of hue, saturation and brightness of the standard color respectively. The position where D is not equal to 0 is marked with color, indicating that facial blemishes appear here.

[0010] The identification and classification module includes a defect classification unit, a feature identification unit and a feature size unit; The defect classification unit is used to classify the defects in real time according to the feature recognition results of the facial defects and in combination with the defect features of the 3D scanner, that is, the round red and swollen facial defects are recorded as acne marks, the irregular shapes are recorded as scar facial defects, and the round brown and round black facial defects are recorded as spots; The characteristic identification unit is used to identify spots of different colors with different colors by using a color identification instrument; The characteristic size unit is used to calculate the size of facial blemishes in real time, and the calculation formula is as follows: The area of ​​a circular spot is: A = N × L × w; Among them, N represents the number of pixels of the circular spot in the image, w represents the actual width corresponding to each pixel, and L represents the actual length corresponding to each pixel.

[0011] The image editing warning terminal includes an image generation module, an image editing adjustment module, an abnormality judgment module and a deviation warning module; The image generation module is used for the image converter to automatically generate a facial image from the facial features of the human face scanned by the 3D scanner; The image editing and adjustment module includes a facial features standard ratio unit and a facial features ratio adjustment unit; The facial features standard proportion unit is used to set standard facial features proportion parameters, that is, standard facial features proportion parameters obtained according to different heights and weights, to obtain a standard facial features proportion image; The facial features ratio adjustment unit is used to adjust the facial features ratio of the automatically generated facial image and the standard facial features ratio image in real time, and the adjustment is performed using touch pens of different sizes.

[0012] The abnormality judgment module is used to judge abnormalities of facial features proportion adjustment in real time by combining multiple directions, multiple angles and multiple positions, as follows: Step 1: Calculate all feature change values ​​in multiple directions, angles and positions , the calculation formula is as follows: ; in, represents the total feature change value, Indicates predefined global scale features, represents the set of adjusted local feature points, Represents the adjustment amount of local feature points, represents the sensitivity of the global scale to local features, and Represent local points The coordinate change of and Represent local points The coordinates of Step 2: If If it is not equal to 0, it means that the proportions of the facial features are abnormal and need to be adjusted. If it is equal to 0, it means that the proportions of facial features are normal and do not need to be adjusted.

[0013] The deviation warning module includes a modification deviation warning unit, which is used to calculate the modification deviation value in real time. The calculation method is as follows: Set every two points on the facial features as P1 (x1, y1, z1) and P2 (x2, y2, z2), and switch between the two points according to the nose, mouth, eyebrows, ears and eyes to calculate the deviation value of the retouching The calculation formula is as follows: ; Set the deviation threshold. If If the absolute value of is greater than the deviation threshold, it means that the image editing has deviated, and the reporting system issues an abnormal image editing warning reminder. If not, it means that the image editing is normal.

[0014] The abnormal correction end includes a data receiving module, a global calculation module, a correction synchronization module and a correction evaluation module; The data receiving module is used to receive the local image repair abnormality judgment result of image processing in real time through a data receiver.

[0015] The global calculation module includes a local deviation influencing unit and a global calculation adjustment unit; The local deviation influence unit is used to perform progressive deviation correction values ​​on the facial features in the order of distance from small to large. The deviation correction value calculation method is as follows: S1: Calculate the coefficient of the first-order term of the regression equation. The formula is as follows: ; in, is the linear coefficient and represents the quadratic relationship between the pitch angle value of the first editing point and the correction angle data, as follows: Indicates the base value of the first correction angle deviation. Indicates the basic value of the deviation of the first correction angle to the specified point. Indicates the base value of the second correction angle deviation. Indicates the base value of the second correction angle to reach the specified point. Indicates the basic value of the correction angle deviation at the current moment. Here, it indicates the deviation values ​​at different time points and different correction angles. The correction angle indicates the pitch correction angle of the facial features and the correction angle between the facial features and the corresponding correction point. S2: Calculate the quadratic coefficient of the regression equation. The formula is as follows: ; in, represents the coefficient of the quadratic term, and represents the binary quadratic relationship between the pitch angle value of the first editing point and the correction angle data; ; in, represents the constant term coefficient, Indicates the average value of the correction angle deviation at the current moment. The correction angle here indicates the corrected pitch angle and the correction angle between the corrected angle and the specified correction point. Indicates The deviation value of the correction angle, It represents the average value of all deformation data in the pitch angle data set when the image editing point is corrected. represents the average value of all corrected angle data in the corrected angle data set. Indicates The deviation value of the correction angle of a correction point, that is, the sum of the deviation values ​​of the correction angles of a correction point and several points of the facial features; S3: establishing a regression equation based on the linear term coefficient, the quadratic term coefficient and the constant term coefficient, thereby obtaining the correction deviation value of the pitch angle between the multiple facial features during image correction, and setting the correction safety parameter; The global calculation adjustment unit is used to adjust the image repairing points of the global features in real time according to the progressive deviation correction value calculated by the local deviation influencing unit.

[0016] The correction synchronization module is used to track the progressive deviation correction value in real time through a data tracker, and synchronously perform image correction adjustment according to the progressive deviation correction value; The correction evaluation module is used to perform image processing evaluation in real time after the deviation correction of the retouched image. The evaluation method is to calculate the difference between the facial feature proportion parameters after the deviation correction and the standard facial feature proportion parameters. If the difference after proportional enlargement or reduction is less than or equal to 0.02mm, it means that the proportion of facial features after image processing is normal. If the difference after proportional enlargement or reduction is greater than 0.02mm, it means that the proportion of facial features after image processing is abnormal. When the proportion of facial features after retouching is abnormal, it will be reported to the system and the abnormal position will be automatically displayed through the page display.

[0017] The present invention has the following beneficial effects: 1. In the present invention, by setting a defect recognition end, during the facial feature recognition operation based on image processing, by arbitrarily switching between local scanning and full scanning of the face, multi-region recognition of facial features is performed in real time, the positions of facial features are determined in real time, and facial defects are determined in real time during multi-region recognition. Facial defects include spots, spot colors, scars, acne marks and lines. Facial defects are classified in real time, the positions of defects are marked with features, and the feature sizes are calculated in real time. Therefore, when the system is performing facial feature recognition, it can accurately identify the spots and defects on the face, avoid the inability to effectively identify the spots and defects on the faces of twins when performing facial recognition, prevent deviations in facial feature recognition, and further reduce facial feature recognition errors.

[0018] 2. In the present invention, by setting a photo editing warning terminal, during the face feature recognition operation based on image processing, a face image is automatically generated through the recognition results of the face facial features, the facial features of the image are adjusted in real time according to the standard facial features ratio, and it is judged in real time whether the adjustment of local features affects all features, and the abnormal judgment of facial features ratio adjustment is made in real time in multiple directions, angles and positions, and when it is judged that the local adjustment of facial features ratio affects all features, a photo editing deviation warning reminder is made in real time, so that when the system is performing photo editing, it can judge whether the photo editing is abnormal according to the adjusted ratio after the photo editing, avoid local deviation of the photo editing after the face feature recognition, and can promptly remind the photo editor of the abnormal facial features photo editing in case of local deviation, thereby improving the convenient photo editing effect of image processing.

[0019] 3. In the present invention, by setting an abnormal correction end, during the facial feature recognition operation based on image processing, the local photo editing abnormality judgment result of the image processing is received in real time through the data receiver, and the photo editing correction value is calculated in real time in multiple directions, multiple angles and multiple positions when a local deviation occurs in the photo editing, so that all corrections and adjustments can be made in real time when there is a local deviation in the image processing, preventing the local photo editing from affecting the processing effect of the overall image, solving the problem that manual observation cannot accurately identify the photo editing deviation, and synchronously performing photo editing adjustments through global calculation, so that the system can perform overall correction of the face according to the local deviation, thereby improving the efficiency of image processing, and performing photo editing evaluation in real time after the image is corrected, so that the photo editing feedback can be clearly known after the image processing, avoiding the problem of deviation in manual photo editing affecting the overall facial features, and reducing the complexity of manual photo editing. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the overall system architecture of a facial feature recognition system based on digital image processing according to the present invention; Figure 2 A schematic diagram of the structure of a defect recognition terminal of a face feature recognition system based on digital image processing according to the present invention; Figure 3 It is a schematic diagram of the structure of a photo editing warning terminal of a face feature recognition system based on digital image processing according to the present invention; Figure 4 The present invention is a schematic diagram of the structure of an abnormality correction end of a facial feature recognition system based on digital image processing. DETAILED DESCRIPTION

[0021] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0022] Embodiment 1 Please refer to Figure 1 to Figure 2 As shown: A facial feature recognition system based on digital image processing, including a defect recognition end, a photo editing warning end and an abnormality correction end; The defect recognition end is used to perform multi-region recognition of facial features of a person in real time by switching between partial scanning and full scanning of the face at will, determine the positions of the facial features of the person, and determine facial defects of the person in real time during multi-region recognition; The image editing warning terminal is used to automatically generate a face image based on the recognition results of the face features, adjust the facial features of the image in real time according to the standard facial features ratio, and judge in real time whether the adjustment of local features affects all features. It judges abnormalities of facial features ratio adjustment in real time from multiple directions, angles and positions, and issues a real-time image editing deviation warning when it is judged that the local adjustment of facial features ratio affects all features. The abnormality correction end is used to receive the local photo editing abnormality judgment results of image processing in real time through the data receiver, and to calculate the photo editing correction values ​​in real time in multiple directions, multiple angles and multiple positions when local deviations occur in the photo editing, so that all correction adjustments can be made in real time when local deviations occur in the image processing, and photo editing evaluation can be performed in real time after the image is corrected.

[0023] The defect recognition end includes a local switching module, a multi-effect recognition module and a recognition classification module; The local switching module includes a local scanning unit, a full scanning unit and a facial features recognition and positioning unit; The local scanning unit is used to collect local features of a human face in real time through a 3D facial scanner, the local features including eyeball color, eyeball texture, mouth color, mouth thickness, eyebrow shape, eyebrow length, eyebrow width, cheek color, cheek shape, philtrum length, philtrum width, spots, scars, acne marks, spot color, scar size, scar color, acne mark size, acne mark color, ear color and ear shape, and record the local features of the human face in real time through a data recorder; The full scanning unit is used to collect all features of a person's face in real time through a 3D facial scanner, including nose, eyes, mouth, eyebrows, cheeks, philtrum and ears, and record all features of the person's face in real time through a data recorder; The facial features recognition and positioning unit is used to set the facial features model of the human face, determine the real-time positions of the facial features of the human face in real time according to the scanning results of the 3D scanner, and display the facial features in real time through the local display page.

[0024] The multi-effect recognition module is used to determine facial blemishes in real time during multi-region recognition. Facial blemishes include spots, spot colors, scars and acne marks. It calculates the color features of the face in real time and calculates the average value of facial blemishes. , the average value of saturation and the average value of brightness As the color feature of the facial blemish, the calculation formula is as follows: ; ; ; Among them, H represents hue, S represents saturation, and V represents brightness, which represents the color characteristic component of facial blemishes. , and They are The hue, saturation, and lightness values ​​of each pixel; Set the standard color features of facial features and calculate the difference between the color of facial blemishes and the standard color features. The calculation formula is as follows: ; Where D represents the difference between the color of facial blemishes and the standard color feature. , and They are the average values ​​of hue, saturation and brightness of the standard color respectively. The position where D is not equal to 0 is marked with color, indicating that facial blemishes appear here.

[0025] The recognition and classification module includes a defect classification unit, a feature identification unit and a feature size unit; The defect classification unit is used to classify the defects in real time according to the feature recognition results of facial defects and in combination with the defect features of the 3D scanner, that is, the round red and swollen facial defects are recorded as acne marks, the irregular shapes are recorded as scar facial defects, and the round brown and round black facial defects are recorded as spots; The feature identification unit is used to identify spots of different colors with different colors through a color identification instrument; The feature size unit is used to calculate the size of facial blemishes in real time. The calculation formula is as follows: The area of ​​a circular spot is: A = N × L × w; Among them, N represents the number of pixels of the circular spots in the image, w represents the actual width corresponding to each pixel, and L represents the actual length corresponding to each pixel. By classifying facial blemishes in real time, identifying the locations of blemishes, and calculating the feature sizes in real time, the system can accurately identify spots and blemishes on the face when performing facial feature recognition, avoid the inability to effectively identify spots and blemishes on the faces of twins when performing face recognition, prevent deviations in facial feature recognition, and further reduce facial feature recognition errors.

[0026] Embodiment 2 Please refer to Figure 3 As shown: Based on the first embodiment, the image editing warning terminal includes an image generation module, an image editing adjustment module, an abnormality judgment module and a deviation warning module; The image generation module is used for the image converter to automatically generate a facial image from the facial features of the human face scanned by the 3D scanner; The photo editing and adjustment module includes a facial features standard proportion unit and a facial features proportion adjustment unit; The facial features standard proportion unit is used to set standard facial features proportion parameters, that is, standard facial features proportion parameters obtained according to different heights and weights, to obtain standard facial features proportion images; The facial features proportion adjustment unit is used to make real-time adjustment of the facial features proportions between the automatically generated facial image and the standard facial features proportion image, using touch pens of different sizes for the adjustment.

[0027] The abnormality judgment module is used to judge abnormalities of facial features proportion adjustment in real time by combining multiple directions, multiple angles and multiple positions. The details are as follows: Step 1: Calculate all feature change values ​​in multiple directions, angles and positions , the calculation formula is as follows: ; in, represents the total feature change value, Indicates predefined global scale features, represents the set of adjusted local feature points, Represents the adjustment amount of local feature points, represents the sensitivity of the global scale to local features, and Represent local points The coordinate change of and Represent local points The coordinates of Step 2: If If it is not equal to 0, it means that the proportions of the facial features are abnormal and need to be adjusted. If it is equal to 0, it means that the proportions of facial features are normal and do not need to be adjusted.

[0028] The deviation warning module includes a modification deviation warning unit, which is used to calculate the modification deviation value in real time. The calculation method is as follows: Set every two points on the facial features as P1 (x1, y1, z1) and P2 (x2, y2, z2), and switch between the two points according to the nose, mouth, eyebrows, ears and eyes to calculate the deviation value of the retouching The calculation formula is as follows: ; Set the deviation threshold. If If the absolute value of is greater than the deviation threshold, it means that the photo editing has deviated, and the reporting system will issue a warning reminder for abnormal photo editing. If not, it means that the photo editing is normal. The abnormal judgment of facial feature proportion adjustment is performed in real time in multiple directions, angles, and positions. When it is judged that the local adjustment of the facial feature proportion affects all features, a warning reminder for photo editing deviation is issued in real time. When the system is performing photo editing, it can judge whether the photo editing is abnormal in real time according to the adjusted proportion after editing, so as to avoid local deviation of photo editing after facial feature recognition.

[0029] Embodiment 3 Please refer to Figure 4 As shown: Based on the first embodiment, the abnormal correction end includes a data receiving module, a global calculation module, a correction synchronization module and a correction evaluation module; The data receiving module is used to receive the local image repair abnormality judgment result of image processing in real time through a data receiver.

[0030] The global calculation module includes a local deviation influence unit and a global calculation adjustment unit; The local deviation influence unit is used to make progressive deviation corrections to the facial features in the order of distance from small to large. It can also make edge deviation correction calculations to the edges of the facial features according to the following deviation correction values, so that the accuracy of facial feature recognition is higher, accurate to the point-to-point angle deviation. The deviation correction value calculation method is as follows: S1: Calculate the coefficient of the first-order term of the regression equation. The formula is as follows: ; in, is the linear coefficient and represents the quadratic relationship between the pitch angle value of the first editing point and the correction angle data, as follows: Indicates the base value of the first correction angle deviation. Indicates the basic value of the deviation of the first correction angle to the specified point. Indicates the base value of the second correction angle deviation. Indicates the base value of the second correction angle to reach the specified point. Indicates the basic value of the correction angle deviation at the current moment. Here, it indicates the deviation values ​​at different time points and different correction angles. The correction angle indicates the pitch correction angle of the facial features and the correction angle between the facial features and the corresponding correction point. S2: Calculate the quadratic coefficient of the regression equation. The formula is as follows: ; in, represents the coefficient of the quadratic term, and represents the binary quadratic relationship between the pitch angle value of the first editing point and the correction angle data; ; in, represents the constant term coefficient, Indicates the average value of the correction angle deviation at the current moment. The correction angle here indicates the corrected pitch angle and the correction angle between the corrected angle and the specified correction point. Indicates The deviation value of the correction angle, It represents the average value of all deformation data in the pitch angle data set when the image editing point is corrected. Represents the average value of all corrected angle data in the corrected angle data set. Indicates The deviation value of the correction angle of the correction point; S3: establishing a regression equation based on the linear term coefficient, the quadratic term coefficient and the constant term coefficient, thereby obtaining the correction deviation value of the pitch angle between the multiple facial features during image correction, and setting the correction safety parameter; The global calculation adjustment unit is used to adjust the retouching points of the global features in real time according to the progressive deviation correction value calculated by the local deviation influence unit. The adjustment of the face data here is a legal adjustment, which is designed to avoid distortion after facial retouching.

[0031] The correction synchronization module is used to track the progressive deviation correction value in real time through a data tracker, and synchronously perform image correction adjustment according to the progressive deviation correction value; The correction evaluation module is used to perform image processing evaluation in real time after the deviation of the retouching is corrected. The evaluation method is to calculate the difference between the facial feature proportion parameters after the deviation correction and the standard facial feature proportion parameters. If the difference after proportional enlargement or reduction is less than or equal to 0.02mm, it means that the facial feature proportions after image processing are normal. If the difference after proportional enlargement or reduction is greater than 0.02mm, it means that the facial feature proportions after image processing are abnormal. When the facial feature proportions after the retouching are abnormal, the system is reported, and the abnormal position is automatically displayed through the page display to prevent local retouching from affecting the processing effect of the overall image, which solves the problem that manual observation cannot accurately identify the deviation of the retouching, and synchronously performs retouching adjustment through global calculation, so that the system can perform overall correction of the face according to the local deviation, improve the efficiency of image processing, and perform retouching evaluation in real time after the image is corrected, so that the retouching feedback can be clearly known after the image processing. The collection and use of face data involved in the present invention are both legal collection and use.

[0032] The present invention provides a facial feature recognition system based on digital image processing. When the system is in operation, the system performs multi-region recognition of facial features in real time by switching between partial scanning and full scanning of the face at will, determines the positions of facial features in real time, and determines facial defects in real time during multi-region recognition. Facial defects include spots, spot colors, scars, acne marks and lines. The facial defects are classified in real time, the positions of the defects are marked with features, and the feature sizes are calculated in real time, so that the system can accurately identify the spots and defects on the face when performing facial feature recognition, avoid the inability to effectively recognize the spots and defects on the faces of twins when performing face recognition, prevent the deviation of facial feature recognition, and further reduce the error of facial feature recognition; the facial image of the face is automatically generated through the recognition result of the facial features, the facial features of the image are adjusted in real time according to the standard proportion of the facial features, and it is judged in real time whether the adjustment of the local features affects all the features, and the abnormal judgment of the facial features proportion adjustment is performed in real time in multiple directions, multiple angles and multiple positions, and when judging the influence of the local adjustment of the facial features proportion, the system can judge the abnormality of the facial features proportion adjustment in real time. When all features are affected, a warning reminder for deviation in image editing is given in real time, so that when the system is performing image editing, it can judge whether the image editing is abnormal according to the adjustment ratio after the image editing in real time, avoid local deviation of the image editing after facial feature recognition, and can promptly remind the retoucher of the abnormal image editing of human facial features in case of local deviation, thereby improving the convenient image editing effect of image processing; the local image editing abnormality judgment result of image processing is received in real time through a data receiver, and all multi-directional, multi-angle and multi-position real-time calculation of image editing correction values ​​is performed in real time when local deviation occurs in the image editing, so that all corrections and adjustments can be made in real time when local deviation occurs in the image processing, preventing local image editing from affecting the processing effect of the overall image, solving the problem that manual observation cannot accurately identify the deviation of image editing, and synchronously performing image editing adjustments through global calculation, so that the system can perform overall correction of the face according to the local deviation, thereby improving the efficiency of image processing, and by performing real-time image editing evaluation after image correction, the image editing feedback can be clearly known after image processing, thereby avoiding the problem of deviation in manual image editing affecting the overall facial features, and reducing the complexity of manual image editing.

[0033] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A facial feature recognition system based on digital image processing, characterized in that: The facial feature recognition system based on digital image processing includes a defect recognition terminal, a photo editing warning terminal and an abnormality correction terminal; The defect recognition end is used to perform multi-region recognition of facial features of a person's face in real time by switching between partial scanning and full scanning of the face at will, determine the positions of the facial features of the person's face, and determine facial defects of the person's face in real time during multi-region recognition; The image editing warning terminal is used to automatically generate a face image of a human face based on the recognition result of the facial features of the human face, adjust the facial features of the image in real time according to the standard facial features ratio, and judge in real time whether the adjustment of local features affects all features, judge abnormalities of the facial features ratio adjustment in real time from multiple directions, angles and positions, and give a real-time image editing deviation warning reminder when it is judged that the local adjustment of the facial features ratio affects all features; The abnormality correction end is used to receive the local photo editing abnormality judgment results of the image processing in real time through the data receiver, and to perform real-time calculation of the photo editing correction values ​​in multiple directions, multiple angles and multiple positions when local deviations occur in the photo editing, so that all correction adjustments can be made in real time when local deviations occur in the image processing, and the photo editing evaluation can be performed in real time after the image is corrected.

2. The system according to claim 1, characterized in that The defect recognition terminal includes a local switching module, a multi-effect recognition module and a recognition classification module; The local switching module includes a local scanning unit, a full scanning unit and a facial features recognition and positioning unit; The local scanning unit is used to collect local features of the human face in real time through a 3D facial scanner, the local features including eyeball color, eyeball texture, mouth color, mouth thickness, eyebrow shape, eyebrow length, eyebrow width, cheek color, cheek shape, philtrum length, philtrum width, spots, scars, acne marks, spot color, scar size, scar color, acne mark size, acne mark color, ear color and ear shape, and record the local features of the human face in real time through a data recorder; The full scanning unit is used to collect all features of the human face in real time through a 3D facial scanner, including nose, eyes, mouth, eyebrows, cheeks, philtrum and ears, and record all features of the human face in real time through a data recorder; The facial features recognition and positioning unit is used to set the facial features model of the human face, determine the real-time positions of the facial features of the human face in real time according to the scanning results of the 3D scanner, and display the facial features in real time through a local display page.

3. The system according to claim 2, characterized in that The multi-effect recognition module is used to determine the facial blemishes of the human face in real time during multi-region recognition. Facial blemishes include spots, spot colors, scars and acne marks. The color features of the human face are calculated in real time, and the average value of the facial blemishes is calculated. , the average value of saturation and the average value of brightness As the color feature of the facial blemish, the calculation formula is as follows: ； ； ； Among them, H represents hue, S represents saturation, and V represents brightness, which represents the color characteristic component of facial blemishes. , and They are The hue, saturation, and lightness values ​​of each pixel; Set the standard color features of facial features and calculate the difference between the color of facial blemishes and the standard color features. The calculation formula is as follows: ； Where D represents the difference between the color of facial blemishes and the standard color feature. , and They are the average values ​​of hue, saturation and brightness of the standard color respectively. The position where D is not equal to 0 is marked with color, indicating that facial blemishes appear here.

4. The system according to claim 3, characterized in that The identification and classification module includes a defect classification unit, a feature identification unit and a feature size unit; The defect classification unit is used to classify the defects in real time according to the feature recognition results of the facial defects and in combination with the defect features of the 3D scanner, that is, the round red and swollen facial defects are recorded as acne marks, the irregular shapes are recorded as scar facial defects, and the round brown and round black facial defects are recorded as spots; The characteristic identification unit is used to identify spots of different colors with different colors by using a color identification instrument; The characteristic size unit is used to calculate the size of facial blemishes in real time, and the calculation formula is as follows: The area of ​​a circular spot is: A = N × L × w; Among them, N represents the number of pixels of the circular spot in the image, w represents the actual width corresponding to each pixel, and L represents the actual length corresponding to each pixel.

5. The system according to claim 1, characterized in that The image editing warning terminal includes an image generation module, an image editing adjustment module, an abnormality judgment module and a deviation warning module; The image generation module is used for the image converter to automatically generate a facial image from the facial features of the human face scanned by the 3D scanner; The image editing and adjustment module includes a facial features standard ratio unit and a facial features ratio adjustment unit; The facial features standard proportion unit is used to set standard facial features proportion parameters, that is, standard facial features proportion parameters obtained according to different heights and weights, to obtain a standard facial features proportion image; The facial features ratio adjustment unit is used to adjust the facial features ratio of the automatically generated facial image and the standard facial features ratio image in real time, and the adjustment is performed using touch pens of different sizes.

6. The system according to claim 5, characterized in that The abnormality judgment module is used to judge abnormalities of facial features proportion adjustment in real time by combining multiple directions, multiple angles and multiple positions, as follows: Step 1: Calculate all feature change values ​​in multiple directions, angles and positions , the calculation formula is as follows: ； in, represents the total feature change value, Indicates predefined global scale features, represents the set of adjusted local feature points, Represents the adjustment amount of local feature points, represents the sensitivity of the global scale to local features, and Represent local points The coordinate change of and Represent local points The coordinates of Step 2: If If it is not equal to 0, it means that the proportions of the facial features are abnormal and need to be adjusted. If it is equal to 0, it means that the proportions of facial features are normal and do not need to be adjusted.

7. The system according to claim 6, characterized in that The deviation warning module includes a modification deviation warning unit, which is used to calculate the modification deviation value in real time. The calculation method is as follows: Set every two points on the facial features as P1 (x1, y1, z1) and P2 (x2, y2, z2), and switch between the two points according to the nose, mouth, eyebrows, ears and eyes to calculate the deviation value of the retouching The calculation formula is as follows: ； Set the deviation threshold. If If the absolute value of is greater than the deviation threshold, it means that the image editing has deviated, and the reporting system issues an abnormal image editing warning reminder. If not, it means that the image editing is normal.

8. The system according to claim 1, characterized in that The abnormal correction end includes a data receiving module, a global calculation module, a correction synchronization module and a correction evaluation module; The data receiving module is used to receive the local image repair abnormality judgment result of image processing in real time through a data receiver.

9. The system according to claim 8, characterized in that The global calculation module includes a local deviation influencing unit and a global calculation adjustment unit; The local deviation influence unit is used to perform progressive deviation correction values ​​on the facial features in the order of distance from small to large. The deviation correction value calculation method is as follows: S1: Calculate the coefficient of the first-order term of the regression equation. The formula is as follows: ； in, is the linear coefficient and represents the quadratic relationship between the pitch angle value of the first editing point and the correction angle data, as follows: Indicates the base value of the first correction angle deviation. Indicates the basic value of the deviation of the first correction angle to the specified point. Indicates the base value of the second correction angle deviation. Indicates the base value of the second correction angle to reach the specified point. Indicates the basic value of the correction angle deviation at the current moment. Here, it indicates the deviation values ​​at different time points and different correction angles. The correction angle indicates the pitch correction angle of the facial features and the correction angle between the facial features and the corresponding correction point. S2: Calculate the quadratic coefficient of the regression equation. The formula is as follows: ； in, represents the coefficient of the quadratic term, and represents the binary quadratic relationship between the pitch angle value of the first editing point and the correction angle data; ； in, represents the constant term coefficient, Indicates the average value of the correction angle deviation at the current moment. The correction angle here indicates the corrected pitch angle and the correction angle between the corrected angle and the specified correction point. Indicates The deviation value of the correction angle, It represents the average value of all deformation data in the pitch angle data set when the image editing point is corrected. represents the average value of all corrected angle data in the corrected angle data set. Indicates The deviation value of the correction angle of the correction point; S3: establishing a regression equation based on the linear term coefficient, the quadratic term coefficient and the constant term coefficient, thereby obtaining the correction deviation value of the pitch angle between the multiple facial features during image correction, and setting the correction safety parameter; The global calculation adjustment unit is used to adjust the image repairing points of the global features in real time according to the progressive deviation correction value calculated by the local deviation influencing unit.

10. The system according to claim 9, characterized in that The correction synchronization module is used to track the progressive deviation correction value in real time through a data tracker, and synchronously perform image correction adjustment according to the progressive deviation correction value; The correction evaluation module is used to perform image processing evaluation in real time after the deviation correction of the retouched image. The evaluation method is to calculate the difference between the facial feature proportion parameters after the deviation correction and the standard facial feature proportion parameters. If the difference after proportional enlargement or reduction is less than or equal to 0.02mm, it means that the proportion of facial features after image processing is normal. If the difference after proportional enlargement or reduction is greater than 0.02mm, it means that the proportion of facial features after image processing is abnormal. When the proportion of facial features after retouching is abnormal, it will be reported to the system and the abnormal position will be automatically displayed through the page display.

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