JPH0667320A - Method for extracting data of person's face and method for determining exposure - Google Patents

Abstract

PURPOSE:To extract the data on a person's face from an image where the person's face and the ground surface, wood, etc., resembling the hues of the person's face coexist. CONSTITUTION:The person's face is divided to many pieces and is metered. The metered data is converted to a hue value H and a saturation value S, from which a two-dimensional histogram of HS is formed (100 to 104). The two-dimensional histogram is divided to each of the peaks of single peaks (106). To which of the divided peaks the respective picture elements of the original image belong is judged and the picture elements are divided to the groups corresponding to the divided peaks. The image is divided by each of the respective groups and a candidate region is selected (108). Whether the person's face or not is judged from the shape of the nearby region existing near the candidate region and the photometric data of the region judged to be the person's face is outputted (110).

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for extracting data on a human face and a method for determining an exposure amount, and more specifically, it relates to a human face image used for copying a color original image on a color copying material or black-and-white copying material. The present invention relates to a method for extracting face density data and an exposure amount determination method using this method.

[0002]

2. Description of the Related Art When viewing a portrait photograph, the most noticeable part is the person's face, and in order to create a high quality photograph, the person's face color should be adjusted appropriately. It is necessary to burn it in a different color.

Conventionally, a face area in an original image on a color film is designated by a light pen to extract density data of a person's face, and the color of the face is properly printed based on the extracted density data. The amount of exposure is determined. Examples of such a technique include JP-A-62-115430, JP-A-62-115431, and JP-A-62-1154.
32, JP-A-62-189456, JP-A-62-189457, and JP-A-63-138340.
JP-A-63-178222.

However, in the above-mentioned conventional technique, the operator has to specify the face area with a light pen for each image, and therefore, there is a problem that the printing operation takes time. Further, since the operator has to visually specify the face area, it is difficult to unmanned.

Further, Japanese Patent Application Laid-Open No. 52-156624,
JP-A-52-156625, JP-A-53-123
30, JP-A-53-145620, JP-A-53-145621, and JP-A-53-145622.
The publication describes the following method of extracting the data of a person's face by extracting the skin color data. That is, the color original image is divided into a large number of photometric points, and each photometric point is decomposed into three colors of R (red), G (green), and B (blue) for photometry, and each photometric point calculated from the photometric data. It is determined whether the color of is within the skin color range. Then, the cluster (group) of photometric points determined to be in the skin color range is used as face density data. However, in this method, it is assumed that the color within the skin color range is the density data of the face, and thus the parts other than the face having the skin color such as the ground, the trunk of the tree, and the clothes or the color close to the skin color are also the density data of the face. It will be extracted. Even if the same subject is shot under the same conditions, the tint of the shot image differs depending on the film type, and therefore the density data of the face may not be automatically extracted if the film type is different. Furthermore, when the color of the light source that illuminates the subject is different, the tint of the shot image is different (for example, the image shot with a fluorescent lamp as the light source is greenish), so if the light source color is different, the face density data May not be automatically extracted.

In order to solve the problem caused by the difference in the light source color, the light source color is corrected and then the photometric data in the skin color range is extracted. As a light source,
It can be roughly classified into sunlight, fluorescent light, and tungsten light, but sunlight has different tints depending on the season and time zone, and even if the season and time zone are the same, the tint differs depending on whether it is direct light or indirect light. In addition, artificial light such as a fluorescent lamp has various colors with the diversification of products. Therefore, it is difficult to perform the light source correction by specifying the light source type for each of the light sources. Further, even if the light source correction is perfectly performed, it is not possible to prevent extraction of the skin color such as the ground or a tree trunk or a portion similar to the skin color. Can not.

The present invention has been made to solve the above-mentioned problems, and characteristic image data extraction capable of automatically extracting only human face data from a color original image such as a negative film with high accuracy. It is an object of the present invention to provide a method and an exposure amount determination method using this method.

[0008]

In order to achieve the above object, the invention of claim 1 divides a color original image into a large number of pixels and decomposes each pixel into three colors of red light, green light and blue light. Meter the light, find the histogram of the hue value based on the data obtained by the photometry, divide the obtained histogram for each mountain,
It is determined which of the divided mountains each pixel of the color original image belongs to, the pixels are divided into groups corresponding to the divided mountains, and the color original image is divided into each group, and the divided areas are divided. It is determined whether or not the selected region is a person's face based on the shape of a region in the vicinity of the selected region, and data of the region determined to be the person's face is extracted.

According to a second aspect of the invention, the color original image is divided into a large number of pixels, each pixel is decomposed into three colors of red light, green light and blue light, and photometry is performed. Based on the data obtained by the photometry. To obtain a two-dimensional histogram for the hue value and the saturation value, divide the obtained two-dimensional histogram for each mountain, and determine which of the divided mountains each pixel of the color original image belongs to and determine a large number of pixels. The color original image is divided into groups corresponding to the divided mountains, each group is divided, and at least one of the divided areas is selected to be selected based on the shape of the area in the vicinity of the selected area. It is determined whether or not the region is a human face, and the data of the region determined to be the human face is extracted.
In each of the above inventions, it is possible to determine whether or not the selected region is a person's face based on the shape and color information of the neighboring region.

[0010]

According to the first aspect of the invention, the color original image is divided into a large number of pixels, each pixel is decomposed into three colors of red light, green light and blue light, and photometry is performed, and the hue is calculated based on the data obtained by the photometry. Find a histogram of values. Next, the obtained histogram is divided for each mountain with a valley or a skirt of the mountain as a boundary. This defines the hue value range of each mountain. Next, by determining which hue value range the hue value of each pixel belongs to, it is determined which of the divided mountains each pixel belongs to, and a group (corresponding to a mountain in which a large number of pixels are divided ( Cluster). Then, the color original image is divided into areas corresponding to the divided groups. At this time, pixels included in the same group may be divided into different regions, but pixels included in different groups are not included in the same region.
As a result, the color original image is divided into regions each including a pixel having a hue value within the hue value range divided by the histogram. Therefore, one area on the color original image includes pixels whose hue value is within a predetermined range, and if at least one area representing the face of a person is selected, the data of the selected area is obtained. Since it represents the face data of the person, the face data of the person can be extracted by selecting the region. Since the face of a person exists above the body and below the head, if the shape of the region in the vicinity of the selected area is judged to determine whether the body or the head is present, it is judged that it is the face of the person. It can be determined whether or not.

If there is a change in film type or light source type, a change over time, a difference in film development, etc., the tint of the original color image changes uniformly over the entire screen. Since the group formed by each pixel of the image is saved only by changing the position of, the divided area of the color original image does not change even if the tint changes. Therefore, in the present invention,
Even if the tint or color range of the original color image changes due to changes in film type or light source type, changes over time, film development differences, etc., it is possible to extract density data of a person's face.

When the hue of the person's face, which is a characteristic portion of the image, is the same as or similar to the hues of other parts, if the color original image is divided based on the histogram of only the hue value, the characteristic image and other It may not be possible to distinguish it from the part.
Therefore, in the invention of claim 2, a saturation value is further introduced in addition to the hue value, a two-dimensional histogram of the hue value and the saturation value is obtained, and the two-dimensional histogram is divided for each mountain and the color is calculated in the same manner as above. The original image is divided, and it is determined whether or not the selected selection is the human face based on the shape of the region in the vicinity of the divided region, and the human face data is extracted.

According to the present invention, since the hue value and the saturation value are used, it is possible to extract the face data of a person even if there are parts where the hue is the same as or similar to that of the person's face. That is, the hue of a person's face is similar to the skin color part of the ground, trees, etc., but in most cases the saturation is different,
By extracting the face data of the person based on the two-dimensional histogram of the hue value and the saturation value, the face data of the person can be extracted from an image in which the face, the ground, trees, and the like are mixed.

Then, if the exposure amount is determined based on the characteristic image data extracted as described above and a print is made, the face of the person can be printed in an appropriate color.

[0015]

Embodiments of the present invention will now be described in detail with reference to the drawings. In this embodiment, the present invention is applied to an auto printer. As shown in FIG. 1, the auto printer of this embodiment includes a conveyance roller 12 that conveys a color negative film 10. Below the color negative film 10 transported by the transport roller 12, the light source 1
4. A color correction filter 16 such as a light control filter and a diffusion box 18 are arranged in order. In addition, above the negative film 10, the light rays transmitted through the negative film 10 are
Distributing prisms 20 that distribute in directions are arranged.
A projection optical system 22, a black shutter 23 and a color paper (printing paper) 24 are sequentially arranged on one optical path distributed by the distribution prism 20, and a projection optical system 26 and a CCD image sensor are arranged on the other optical path. 28 are arranged in order. This CCD image sensor 28 is
One screen (one frame) of the negative film 10 is divided into a large number of pixels (for example, 256 × 256 pixels), and each pixel is R.
(Red), G (green), and B (blue) are separated into three colors for photometry. The CCD image sensor 28 is connected to a 3 × 3 matrix circuit 34 for sensitivity correction of the CCD image sensor via an amplifier 30 that amplifies the output of the CCD image sensor and an analog-digital (A / D) converter 32. The 3 × 3 matrix circuit 34 is connected to an appropriate exposure amount calculation circuit 40 via a face extraction circuit 36 composed of a microcomputer that stores a program of a routine described below, and at the same time, an average density of one entire screen is displayed. It is connected to an appropriate exposure amount calculation circuit 40 via an average density calculation circuit 38 for calculation. The appropriate exposure amount calculation circuit 40 is connected to the color correction filter 16 via a driver 42 that drives the color correction filter.

Next, the operation of this embodiment will be described. Light source 14
The light beam emitted from the laser beam passes through the color correction filter 16, the diffusion box 18, and the color negative film 10, is distributed by the distribution prism 20, and is received by the CCD image sensor 28 via the projection optical system 26. At this time, the black shirt 23 is closed. By this light reception, the CCD image sensor 28 divides the entire screen into a large number of pixels, decomposes each pixel into R, G, and B colors, and performs photometry, and outputs a photometric data signal. The photometric data signal is amplified by the amplifier 30 and then converted into a digital signal by the A / D converter 32, the sensitivity of the image sensor is corrected by the 3 × 3 matrix circuit 34, and the face extraction circuit 36 and the average density calculation circuit 38. Entered in. This average concentration calculation circuit 3
In 8, the average density of the entire one screen is calculated. The face extraction circuit 36 estimates the human face part in one screen as described below, and outputs R, G, B three-color photometric data of the part estimated to be the face. The exposure amount calculation circuit 40 calculates the exposure amount using the three-color photometric data output from the face extraction circuit 36 and the average density calculated by the average density calculation circuit 38, and the color correction filter 16 is calculated via the driver 42. Is controlled and the black shutter 23 is opened and closed for printing. When the average density calculated by the average density calculation circuit 38 is used, the exposure correction amount for the average density can be calculated. When the exposure correction amount is not calculated, the average density calculation circuit 38 is not necessarily required, and the exposure amount may be calculated directly from the three-color photometric data output from the face extraction circuit 36.

FIG. 2 shows a face extraction routine by the face extraction circuit 36. The noise removal, that is, smoothing, of the three-color photometric data input in step 100 is performed. In the next step 102, the R, G, and B three-color photometric data are converted into H (hue value) and L (L) according to the following equations (1) to (3)
(Brightness value) and S (saturation value).

L = (R + G + B) / 3 ... (1) S = 1-min (r ', g', b ') ... (2) H = H' / 2Pi .... ( 3) However, R, G, and B are standardized so that the minimum value is 0 and the maximum value is 1 as shown in the three-dimensional color coordinates in FIG.
Color photometric data, min () is the minimum value of the numerical values in (), r ', g', b'is r '= R / L, g' = G / L,
It represents b '= B / L. Further, H ′ is given by the following expression (4), and Pi (i is one of R, G, and B) is P in FIG.
Is.

[0019]

[Equation 1]

However,

[0021]

[Equation 2]

In step 104, as shown in FIG. 4A, a two-dimensional histogram of the hue value and the saturation value is obtained by using a coordinate system composed of a hue value axis, a saturation value axis and a pixel number axis which are orthogonal to each other. Then, as will be described later in step 106, the obtained two-dimensional histogram is divided into mountains, that is, clustering of the two-dimensional histogram is performed. In the next step 108, clustered 2
A large number of pixels are clustered based on the peaks of the dimensional histogram, the screen is divided based on this clustering, and areas that are candidates for human faces are extracted from the divided areas. In the next step 110, the face area is estimated from the area extracted as the face candidate, and R, G, B three-color photometric data of the area estimated as the face is output. Then, in step 112, it is judged whether or not the printing of all the frames is completed, and when it is judged that the printing is completed, this routine is ended.

Next, details of steps 106 to 110 will be described. FIG. 5 shows the details of step 106. In step 120, an area to be evaluated is cut out from the two-dimensional histogram of the hue value and the saturation value.
In FIG. 4, one frame is set as the evaluation area for simplification of description. In step 122, it is determined whether there is an evaluation area. When the evaluation area cannot be cut out in step 120, that is, when the evaluation of all areas is completed, there is no evaluation area, and this routine is ended. If there is an evaluation area, the X and Y axes for creating the mountain cutting histogram are determined in step 124. In other words, the evaluation area is rotated around an axis parallel to the pixel number axis, and when the mountains of the histogram are viewed from the side, priority is given to multimodality and the position where the mountains are the sharpest is obtained. Determine the X and Y axes. If the processing time needs to be shortened, the accuracy will be somewhat lowered, but axes that maximize the variance of the histogram may be used as the X and Y axes. In the example of FIG. 4 (1), when four mountains labeled with 1 to 4 are viewed from the side, priority is given to multi-modality and the position where the peaks are sharpest is the position where three mountains are visible. Therefore, the X axis is defined in the direction orthogonal to the viewing direction, and the Y axis is defined in the direction orthogonal to the X axis.

In the next step 126, the two-dimensional histogram is projected on the X and Y axes to create one-dimensional histograms. In the example of FIG. 4 (1), when viewed from the direction orthogonal to the X axis, the peaks with the reference numerals 1 and 2 appear to overlap, so the one-dimensional histogram for the X axis has the peaks and the reference numerals with the reference numeral 3. Mountain with 1 and 2 and mountain with 4
Two peaks appear, and when viewed from the direction orthogonal to the Y axis, the peaks labeled with numbers 1 to 4 appear to overlap, so that one peak appears in the histogram for the Y axis. Next step 1
At 28, the histogram is converted into the evaluation function H (a) by the following equation (5), and the mountains are cut out from the histogram on the X axis based on this evaluation function.

[0025]

[Equation 3]

However, f (a) is the number of pixels when the value (feature amount) in the X-axis direction is a, and x is the displacement from the feature amount a.

That is, the average value T of the evaluation function H (a) is calculated, and the range (valley, skirt existence range) below the average value T of the evaluation function H (a) is calculated. Next, the position of the minimum histogram within this range is set as the valley or skirt of the histogram. Then, a histogram is cut out at the obtained valley or skirt.

Explaining the cutting of the mountain with reference to FIG. 6, when the evaluation function H (a) is obtained from the histogram represented by the solid line SI, it becomes as shown by the broken line in the figure. The range of the average value T or less with respect to the negative portion of the evaluation function H (a) is the range of feature amounts v0 to v1 and v2 to v3. The position where the frequency of the histogram in this range is the minimum is the range v
Av0 = v0 in 0 to v1, av1 in the range v2 to v3
And av0 is obtained as a skirt and av2 is obtained as a valley, and the histogram is cut out at this position.

In step 130, the peaks of the histogram about the Y-axis are cut out in the same manner as the peaks of the histogram about the X-axis. Next step 132
Then, an area where the peaks of the X-axis and Y-axis histograms cut out as described above overlap on the two-dimensional histogram is obtained, and the peaks are cut out from the two-dimensional histogram of the hue value and the saturation value. An area E1 in FIG. 4 (1) shows an example of a mountain cut out as described above.

In the next step 134, it is judged whether or not the mountain cut out from the two-dimensional histogram is a single peak, and if it is not a single peak, steps 124 to 134 are repeated until the mountain cut out from the two-dimensional histogram becomes a single peak. repeat. Region E2 in FIG. 4C shows an example of a single-peak mountain cut out as described above.

In the next step 136, a process (labeling) is performed to attach a label for identifying the cut-out single-peak mountain, and in step 138, the labeled mountain is masked and the process returns to step 120. Then, the above steps are repeated to divide the entire area of the two-dimensional histogram for the hue value and the saturation value into single peaks.

FIG. 7 shows the details of step 108 in FIG. 2. In step 140, the range XR (FIG. 4 (3)) and Y in the X-axis direction of the single-peak mountain divided as described above is obtained. The axial range YR (FIG. 4 (3)) is obtained for each single peak, and it is determined whether the hue value and the saturation value of each pixel of the original image belong to these ranges, and pixel clustering is performed. At the same time, the pixels belonging to the range surrounded by the ranges XR and YR are collected, and the original image is divided so that the collected pixels form one area on the original image. Also, the divided areas are numbered. FIG. 4 (2) shows an example in which the original image is divided, and the pixels in each area denoted by reference numerals 1 to 4 correspond to the unimodal peaks denoted by reference numerals 1 to 4 in FIG. 4 (1). is doing. Pixels belonging to pixels included in the same single-peak mountain in FIG. 4 (1) are divided into different regions in FIG. 4 (2). This is because the pixel has a hue value range and a saturation value range, but the area is divided in FIG. 4 (2).

In the next step 142, the small area is removed by judging the area of the divided area, and the numbering is performed again. In the next step 144, a contraction process for deleting all the boundary pixels of the region to remove one skin, and an expansion process for expanding the boundary pixels in the direction of the background pixels to enlarge the skin by one conversely to the contraction process are performed. It separates a small area that grows up from a large area. In the next step 146, small areas are removed and renumbering is performed in the same manner as in step 142, and in order to separate the weakly bonded areas in step 148, the same contraction and expansion processing is performed, In step 150, the small area is removed and renumbering is performed as described above.

FIG. 8 shows a detailed routine of step 110. In this routine, for each of the candidate areas extracted in step 108, the shape and color information of the attention area and the areas located around the attention area are shown. Whether or not the attention area is a face is determined from the shape and color information of the neighboring area. As shown in FIG. 8, in step 170, the same hue value as the attention area and the saturation value or the hue value and the saturation value similar to the attention area are provided around the attention area, and the size (for example, the horizontal fillet diameter and the vertical fillet) is set. Whether the area corresponding to the hand or foot of the person is extracted by determining whether or not the area in the range of 25 to 100% of the size of the attention area is extracted. to decide. The range to be determined is a range in which the body of a person exists, for example, a range having a radius of 5 times the diameter of a circle having the same area as the area of interest centering on the area of interest. When the surface image information is interrupted, the target range is set to the position where the image information is interrupted in the interrupted direction. If there is a region corresponding to the hand or foot around the region of interest, the flag _Fa is set in step 172.

In the next step 174, it is judged whether or not the area of the body of the person is extracted by judging whether or not there is an area continuous with the attention area and the area corresponds to the body of the person. To do. Since the body of a person is usually symmetrical with respect to the left-right axis and is not symmetrical with respect to the vertical direction and is continuous with the face, there is an area that is continuous with the area of interest and whether that area is symmetrical with respect to the horizontal axis and not symmetrical with respect to the vertical By making the determination, it can be determined whether or not there is a region corresponding to the body of the person. If there is an area corresponding to the body of the person, the flag _Fb is set in step 176.

At the next step 178, it is judged whether or not the head exists by judging the following conditions. The head is adjacent to the face, and when it is integrated with the face, the head becomes an almost circular shape, and since the head, the helmet, and the hair are usually present on the head, the hue or saturation is different from the face. Therefore, for a region adjacent to the region of interest, the ratio of the perimeter of this region to the boundary length of the adjacent portion of the region of interest is 30% or more, or the circularity when the region of interest and the region adjacent to the region are integrated. Will be improved,
Whether the hue value difference between the hue value of the area of interest and the hue value of the area adjacent to the area of interest is large or
Whether or not the head is present can be determined by determining whether the saturation value or the brightness value of the area adjacent to the attention area is smaller than that of the attention area. If it is determined that the area corresponding to the head exists, step 1
At 80, flag F _c is set.

In step 182, the flag F_aAnd hula
Gu F_bJudge whether or not the
When you touch your hands or feet around the area of interest,
There is a region that exists and is continuous with the region of interest corresponds to the body
Area of interest, it is determined that the area of interest is the face of a person.
Then, in step 188, R, G, B photometry of the attention area is performed.
Output the data. That a negative decision was made in step 182
If step 184 then flag F_bAnd flag F_c
Judge whether or not is set. This judgment is uncertain
When you are touched, that is, the area following the area of interest corresponds to your body
The area that is adjacent to the attention area corresponds to the head.
Area of interest, it is determined that the area of interest is the face of a person.
Go to step 188. A negative decision is made in step 184
Flag F in step 186_a, Flag F_bas well as
Flag F_cJudgment is made by judging whether or not is set
If it is determined that the attention area is a human face,
Proceed to 188. Next Step 190
Flag F to judge the range _a, F_b, F_cReset
It

The proper exposure amount calculation circuit 40 is composed of the face extraction circuit 3
6. The R, G, B photometric data of the face area extracted as described above in 6 and the screen average density D _i of one frame calculated by the average density calculation circuit 38 (i = R, G, or B) ) And are used to calculate an appropriate exposure amount E _i according to the following equation, and output to the driver 42. The driver 42 controls the dimming filter 16 by calculating an exposure control value from the appropriate exposure amount E _i .

L _og E _i = LM _i · CS _i · (DN _i −D _i ) + PB _i + LB _i + MB _i + NB _i + K ₁ + K ₂ (6) However, each symbol represents the following.

LM: Magnification slope coefficient, which is preset according to the enlargement magnification determined by the type of negative and the print size.

CS: Color slope coefficient prepared for each type of negative, there are underexposure and overexposure, and it is determined whether the average density of the frames to be printed is under or over the standard negative density value. Either underexposure or overexposure is selected.

DN: Standard negative density value. D: Average density value of print frame.

PB: A correction balance value for a standard color paper, which is determined according to the type of color paper.

LB: For standard print lens. It is a correction lens balance value, and is determined according to the type of printing lens.

MB: Correction value (master balance value) for variations in printer light source and changes in paper developing performance.

NB: Negative balance (color balance) value determined by the characteristics of the negative film.

K ₂ : Color correction amount. K ₁ : A density correction amount represented by the following formula.

[0048]

[Equation 4]

Here, K _a and K _b are constants, and FD is the face area average density. Further, the density correction amount K ₁ in the equation (6) may be used as the correction value obtained by the film inspection apparatus, and the color correction amount K ₂ may be expressed using the face area average density as follows.

[0050]

[Equation 5]

However, K _c is a constant. Further, the density correction amount K ₁ and the color correction amount K ₂ in the equation (6) are used as the correction amounts obtained by the film inspection device, and the average density D _i of the print frame in the equation (6) is the average density FD of the face area. _i
Alternatively, the exposure amount may be obtained instead.

In the present embodiment, since the judgment is made by using the contour of the area and the internal structure, the face data can be extracted from the image in which the faces, the ground, the trees and the like having similar hues are mixed. . In this embodiment, the attention area is the hand or foot,
When determining whether it is a body or a head, a plurality of standard hand or foot images, a plurality of standard body images, and a plurality of standard heads are used as described in the first embodiment. The image may be stored in advance, and the region of interest may be compared with these standard images for determination.

Next, another embodiment of the present invention will be described. In this embodiment, the extracted area is formed into a line figure and it is determined whether or not the attention area is a face based on the shape of the neighboring area and the shape of the attention area located around the attention area.
FIG. 9 shows a face determination routine based on line graphics, in which the line information extraction processing of the area for one screen extracted as described above is performed to convert each area into a line graphic. In step 202, it is determined whether or not there is a line figure representing the shoulder by comparing the standard line figure representing the shoulder of the person stored in advance with the line figure of one screen. If the line figure representing the shoulder does not exist, this routine is ended, and if the line figure representing the shoulder exists, it is determined whether or not the line figure exists above the line figure representing the shoulder. If a line figure exists, it is determined in step 206 that this line figure is a line figure of interest, and it is determined whether a line figure representing a head (for example, a hat, hair, helmet, etc.) exists above the line figure of interest. When the determination in step 206 is affirmative, the line drawing representing the head exists above the line drawing of interest and the line drawing representing the shoulder exists below the line drawing of interest, so the line drawing of interest is the line drawing of the face. The probability is high. Therefore, in step 208, it is judged whether or not the contour of the target line figure is close to the contour of the standard face line figure. When the determination in step 208 is affirmative, it is determined in step 210 that the line-of-interest graphic is a face, and R, G, B photometric data of the area corresponding to this line-of-interest graphic is output. On the other hand, if the determination in step 208 is negative, step 21
In FIG. 2, the lower part of the line figure above the line figure representing the shoulder is determined to be the face, and R, G, B photometric data of this part is output.

In the present embodiment, since it is determined whether or not the face is a shape based on the shape of the region of interest or the like, face data can be extracted from an image in which faces having similar hues, the ground, trees and the like are mixed. You can Further, since the face is not determined using the fine structure of the face, it is possible to determine whether or not the face is a small calculation time even if the resolution of the determination target image is low.

FIG. 10 shows a modified example in which the present invention is applied to an exposure amount determining device separate from a printer or a printer processor. In FIG. 10, parts corresponding to those in FIG. 1 are assigned the same reference numerals and explanations thereof will be omitted. Further, the average density calculation circuit 38 is not always necessary, but instead of this, an integrated transmission density detection circuit for detecting LATD of the entire screen may be used.

In FIG. 11, the face extraction circuit of FIG. 10 is composed of a plurality of face extraction circuits 36 ₁ , 36 ₂ ... 36n, and the exposure amount is calculated by parallel processing. Face extraction circuit 36
_1, 36 ₂ ··· 36n reads an image according to the time chart of FIG. 12, calculates the exposure amount, and outputs the result. In FIG. 13, t ₁ is the image reading time for one frame, t _1
2 is the exposure amount calculation time for one frame, t ₃ is the exposure amount calculation result transfer time for one frame, and t ₂ >> t ₁ and t ₃ . The face extraction circuit 36 ₁ reads one frame of image at t _1
The exposure amount is calculated in ₂ hours, and the calculation result is transferred in t ₃ hours. At the same time when one frame of image is read by the face extraction circuit 36 ₁ , one frame of film is fed and the face extraction circuit 36 1
1 frame image reading by ₂ is started, face extraction circuit 3
The exposure amount calculation of 6 ₁ and the image reading of the face extraction circuit 36 ₂ are performed in parallel, and the same applies to the face extraction circuits 36 ₃ and 36 ₄ hereinafter.
... 36n is processed in parallel.

The time Tp required to process mxn frames in parallel is Tp = m (t ₁ + t ₂ + t ₃ ) + (n-1) t ₁ . On the other hand, the processing time Ts when parallel processing is not performed
Is Ts = m · n (t ₁ + t ₂ + t ₃ ). Therefore,

[0058]

[Equation 6]

Double speeding is possible. This parallel processing device can also be applied to the printer of FIG.

In addition to determining the exposure amount of the photo printing apparatus, the present invention determines the exposure amount of the digital color printer, the copying condition of the copying machine, the exposure amount of the camera, the display condition of the CRT screen, and the hard copy from the magnetic image data. It can also be applied to the determination of the amount of light when creating.

[0061]

As described above, according to the present invention, since the data of the human face is extracted from the shape of the neighboring area divided based on the histogram of the hue value, the change of the film type and the light source type. Even if the tint or the color range of the original color image changes due to changes in film characteristics over time, differences in film development, and the like, it is possible to accurately extract face data of a person.

Further, since the face data of the person is extracted from the shape of the neighboring area divided based on the two-dimensional histogram of the hue value and the saturation value, the characteristic image and the part having the same or similar hue are mixed. Even if it does, the effect that the data of the face of the person can be extracted can be obtained.

Further, in the present invention, since the face of a person is judged from the shape of the neighboring area, even if the face of a person is small, the resolution of the image sensor is small, or there is noise due to the influence of glasses, the person's face is detected. The effect that face data can be extracted is obtained.

[Brief description of drawings]

FIG. 1 is a schematic diagram showing a printer according to one embodiment of the present invention.

FIG. 2 is a flowchart showing a face extraction routine of a face extraction circuit.

FIG. 3 is a diagram showing color coordinates.

FIG. 4 (1) is a diagram showing a two-dimensional histogram of hue values and saturation values. (2) is a diagram showing a state in which the original image is divided. (3) is a diagram showing a state in which a single peak is cut out from a two-dimensional histogram.

5 is a diagram showing details of step 106 of FIG. 2. FIG.

FIG. 6 is a diagram showing a histogram and an evaluation function.

FIG. 7 is a diagram showing details of step 108 in FIG.

FIG. 8 is a diagram showing details of step 110 of FIG.

FIG. 9 is a flowchart of a face estimation routine according to another embodiment of the present invention.

FIG. 10 is a schematic diagram of an exposure amount calculation device to which the present invention is applied.

FIG. 11 is a schematic diagram of an exposure amount calculation device that performs parallel processing by a plurality of face extraction circuits.

FIG. 12 is a diagram showing a time chart of parallel processing.

[Explanation of symbols]

 28 CCD image sensor 30 amplifier 36 face extraction circuit

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[Procedure amendment]

[Submission date] August 17, 1993

[Procedure Amendment 1]

[Document name to be amended] Statement

[Correction target item name] 0063

[Correction method] Change

[Correction content]

Further, in the present invention, since the face of a person is judged from the shape of the neighboring area, even if the face of a person is small, the resolution of the image sensor is small, or there is noise due to the influence of glasses, the person's face is detected. The effect that face data can be extracted is obtained.

[Procedure Amendment 2]

[Document name to be amended] Statement

[Name of item to be corrected] Brief description of the drawing

[Correction method] Change

[Correction content]

[Brief description of drawings]

FIG. 1 is a schematic diagram showing a printer according to one embodiment of the present invention.

FIG. 2 is a flowchart showing a face extraction routine of a face extraction circuit.

FIG. 3 is a diagram showing color coordinates.

FIG. 4 (1) is a diagram showing a two-dimensional histogram of hue values and saturation values. (2) is a diagram showing a state in which the original image is divided. (3) is a diagram showing a state in which a single peak is cut out from a two-dimensional histogram.

5 is a diagram showing details of step 106 of FIG. 2. FIG.

FIG. 6 is a diagram showing a histogram and an evaluation function.

FIG. 7 is a diagram showing details of step 108 in FIG.

FIG. 8 is a diagram showing details of step 110 of FIG.

FIG. 9 is a flowchart of a face estimation routine according to another embodiment of the present invention.

FIG. 10 is a schematic diagram of an exposure amount calculation device to which the present invention is applied.

FIG. 11 is a schematic diagram of an exposure amount calculation device that performs parallel processing by a plurality of face extraction circuits.

FIG. 12 is a diagram showing a time chart of parallel processing.

[Explanation of Codes] 28 CCD image sensor 30 Amplifier 36 Face extraction circuit

Claims (4)

[Claims] 1. A color original image is divided into a large number of pixels, each pixel is decomposed into three colors of red light, green light and blue light and photometry is performed, and a histogram of hue values is obtained based on the data obtained by photometry. , The obtained histogram is divided for each mountain, it is judged which pixel of each pixel of the color original image belongs to the divided mountain, and the pixel is divided into a group corresponding to the divided mountain, and for each group, The color original image is divided, one of the divided areas is selected, and it is determined whether or not the selected area is a person's face based on the shape of the area in the vicinity of the selected area. A method for extracting the data of a human face, which extracts the data of a specified area. 2. A color original image is divided into a large number of pixels, each pixel is decomposed into three colors of red light, green light and blue light, and photometry is performed, and a hue value and a saturation value are obtained based on the data obtained by the photometry. The two-dimensional histogram of is calculated, the obtained two-dimensional histogram is divided for each mountain, and it is determined which pixel of each pixel of the color original image belongs to which mountain is divided. In addition, the original color image is divided for each group, one of the divided areas is selected, and whether or not the selected area is a human face based on the shape of the area in the vicinity of the selected area is selected. This is a method for extracting the data of a person's face, which determines whether or not the area is determined to be the person's face. 3. The method for extracting human face data according to claim 1, wherein it is determined whether the selected region is a human face based on the shape and color information of the neighboring region. 4. An exposure amount determining method for determining an exposure amount to a copy material based on the face data of a person extracted according to claim 1.