JPH06160994A - Method for deciding exposure - Google Patents

Abstract

PURPOSE:To decide exposure so that a color original image can be reproduced by proper density based on data extracted from the color original image as the face of a person. CONSTITUTION:The photometry data of an area which is judged as the face by being divided every color area based on the data obtained by executing the photometry of the original image is read and the average density of the read area is obtained (170-174). When there is only one face on the original image (176), the exposure is calculated based on the obtained average density (186). Besides, when there are the plural faces on the original image and difference between the maximum and the minimum values of the density is under a prescribed value, only an average value is obtained and the exposure is calculated based on the average value (178 and 180). On the other hand, when there are the plural faces on the original image and the difference between the maximum and the minimum values of the density is equal to or higher than the prescribed value (176 and 178), the plural areas judged to be the face are divided into two groups according to the density value (182) and the exposure is calculated based on the average density of the first group whose face reflectivity having the large effect of correction by the face density is comparatively high.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an exposure amount determining method,
In particular, the present invention relates to an exposure amount determining method for extracting a density data of a person's face and determining an exposure amount based on the extracted density data when a color original image is copied on a color copying material or a black-and-white copying material.

[0002]

2. Description of the Related Art When watching a portrait, the most noticeable part is the face of the person, and in order to create a high quality photograph or image, the color of the face Must be printed or displayed in the proper color.

Conventionally, when an image including a person is printed or displayed by a photographic processing device such as a printer, a printing device and an image display device, face density data is extracted and the face is extracted based on the extracted density data. The exposure amount is determined so that the density of is reproduced as an appropriate density that is a target density and the face color is appropriately printed or displayed.

However, in this method, since it is assumed that the reflectance of a person's face is single, a plurality of faces having different reflectances exist in the original color image due to individual differences and interracial differences. In this case, there is a problem in that the face density varies and it is not possible to perform correction for printing or displaying in an appropriate color depending on the face density.

In order to solve this problem, it is conceivable that the average value of the densities of a plurality of faces is corrected as the face density, but as is well known, the reflectance of the face greatly differs between races. That is, as shown in FIG. 14, spectral reflectance characteristics of six types of skins from white race to black race (in the figure, Z1: white-blond, Z2: white-brunette, Z3: Japa
In nese, Z4: Hindu, Z5: Mulato, Z6: the negro), there may be a 7-8 times difference in the racial difference between Caucasian and black. Therefore, when the face density is corrected by simply averaging the densities of a plurality of faces, there is a problem in that the densities of any of the faces cannot be properly adjusted.

The present invention has been made to solve the above-mentioned problems, and it is exposed so that a color original image can be reproduced at an appropriate density from data extracted as a human face from a color original image such as a negative film. It is an object to provide an exposure amount determining method for determining an amount.

[0007]

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. Photometry, and based on the data obtained by photometry, to obtain a color area on the original color image having the same or similar hue or hue and saturation, and determine whether or not the obtained color area is a human face area. If the difference between the maximum value and the minimum value of the obtained density exceeds a predetermined value, the area of the person's face is determined based on the obtained density. It classifies into similar density groups, at least one of the classified density groups is selected, and the exposure amount to the copying material is determined based on the selected density group.

According to a second aspect of the present invention, there is provided the exposure amount determining method according to the first aspect, wherein the density of a density group including a face region in which the reflectance of the face of the person who is the subject is equal to or more than a predetermined value is the reference density. The feature is that the exposure amount to the copying material is determined as described above.

According to a third aspect of the present invention, 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. Hue is based on the data obtained by the photometry. Obtain the histogram of the value, divide the obtained histogram for each mountain, determine which of the divided mountains each pixel of the color original image belongs to, and divide the pixels into groups corresponding to the divided mountains, The color original image is divided for each group, and at least one of the contour and the internal structure of each divided area is determined to determine whether or not it is a human face, and the density of the area determined to be the human face is determined. Obtained, when the difference between the maximum value and the minimum value of the obtained density exceeds a predetermined value, the area of the person's face is classified into similar density groups based on the calculated density, Select at least one and transfer to copy material based on the selected density group To determine the amount of exposure.

According to a fourth aspect of the present invention, 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 is obtained based on the data obtained by the photometry. A two-dimensional histogram of the values and the saturation values is obtained, the obtained two-dimensional histogram is divided for each mountain, and the pixel is divided by judging which of the divided mountains each pixel of the color original image belongs to. In addition to dividing into groups corresponding to mountains, the color original image is divided into each group, and at least one of the contour and internal structure of each divided area is determined to determine whether or not it is a human face. If the difference between the maximum value and the minimum value of the calculated density exceeds a predetermined value, the density of the area determined to be the face is determined, and the area of the person's face is grouped into similar density groups based on the calculated density. Select and select at least one of the classified concentration groups. It was based on the density group to determine the exposure amount to the copy material.

[0011]

According to the invention of claim 1, the color original image is divided into a large number of pixels, each pixel is divided into three colors of red light, green light and blue light, and photometry is carried out, and the color is obtained based on the data obtained by the photometry. A color region having the same or similar hue or hue and saturation is obtained on the original image. Next, it is determined whether or not the obtained color area is a human face area. To determine the face area from the similar color area, it can be determined based on the hue or the histogram of the hue and the saturation as described in claim 3 or 4. Then, the density of the area determined to be the human face area is obtained. When the difference between the maximum value and the minimum value of the calculated density exceeds a predetermined value, the human face area is classified into similar density groups based on the calculated density. The densities of the face regions included in this classified group are similar, that is, close to each other. For example, when there are a plurality of face areas having different densities exceeding a predetermined value on the color original image, the face areas are divided into density groups. At least one of the classified density groups is selected and the exposure amount to the copying material is determined based on the selected density group. Therefore, even if there are face areas of different densities exceeding a plurality of predetermined values on the color original image, the face needs to be corrected by the density of the face without performing the correction based on the densities of all the face areas. It can be corrected by the density of the area. For this correction, as described in claim 2, the density of a density group including the region where the reflectance of the face of the person who is the subject is a predetermined value or more, for example, the average density is applied to the copy material. Exposure dose can be determined. As a result, it is possible to correct the density of a density group that includes a face area that has a high reflectance of the face and that needs correction based on the density of the face.

According to the third aspect of the invention, the color original image is divided into a large number of pixels, each pixel is divided 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, the pixels included in the same group may be divided into different regions,
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 the contour of the person's face and the contours of other parts,
Since the internal structure of a person's face and the internal structure of other parts are obviously different, it is possible to determine whether or not it is the human face by determining at least one of the contour of each region and the internal structure.
As described above, in the present invention, changes in film type and light source type,
A person's face can be determined even if the tint or color range of the original color image changes due to changes over time, film development differences, and the like.

Further, in the invention of claim 4, the 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 Similarly, the color original image is divided, and at least one of the contour and the internal structure of the divided area is judged to judge whether or not it is a human face. As described above, according to the present invention, since the hue value and the saturation value are used, even if the face of the person and the part having the same or similar hue (for example, the ground, the tree, etc.) are mixed, the face of the person is mixed. Data can be extracted. 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,
If the face data of the person is extracted based on the two-dimensional histogram of the hue value and the saturation value, the person's face can be determined from the image in which the face, the ground, the tree, and the like are mixed.

[0014]

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. The proper exposure amount calculation circuit 40 is composed of a microcomputer that stores a program of a routine (FIG. 9) described below.

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, which removes noise from the three-color photometric data input in step 100, that is, performs smoothing. 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.

[0018]

[Equation 1] However,

[0019]

[Equation 2] In step 104, as shown in FIG. 4 (1), a two-dimensional histogram for the hue value and the saturation value is obtained using a coordinate system composed of a hue value axis, a saturation value axis, and a pixel number axis that are orthogonal to each other. In the next step 106, as will be described later, the obtained two-dimensional histogram is divided into mountains, that is, the two-dimensional histogram is clustered. In the next step 108, a large number of pixels are clustered on the basis of the peaks of the clustered two-dimensional histogram, the screen is divided based on this clustering, and a region which is a candidate for a human face is extracted from the divided region. .
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. When the processing time needs to be shortened, the accuracy is somewhat deteriorated, but the 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 the four mountains with the reference numbers 1 to 4 are viewed from the side, priority is given to the multi-modality, and the peaks are the most sharp positions because the three mountains are visible. 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 histograms are 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. Three mountains, a mountain with 1 and 2 and a mountain with symbol 4, appear, and when viewed from the direction orthogonal to the Y axis, the mountains with symbols 1 to 4 appear to overlap, so one dimension about the Y axis. One mountain appears in the histogram. In the next step 128, the histogram is converted into an evaluation function H (a) by the following equation (5), and mountains are cut out from the histogram on the X axis based on this evaluation function.

[0022]

[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 obtained, and the range (valley, skirt existence range) below the average value T of the evaluation function H (a) is obtained. 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.

The cutout of the mountain will be described with reference to FIG. 6. When the evaluation function H (a) is obtained from the histogram represented by the solid line SI, it is 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 mountain peak of the Y-axis is cut out in the same manner as the mountain peak cut of the X-axis. Next step 132
Then, an area in which the peaks of the one-dimensional histogram for the X-axis and the Y-axis cut out as described above overlap on the two-dimensional histogram is obtained, and the peak is cut out from the two-dimensional histogram of the hue value and the saturation value. Area E in FIG. 4 (1)
Reference numeral 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 are included in the single-peaked mountains denoted by reference numerals 1 to 4 in FIG. 4 (1). Corresponds to the pixels that are displayed. Pixels belonging to the same single-peak mountain in FIG. 4 (1) are divided into different regions in FIG. 4 (2). This is due to the hue value range of the single-peak mountain in FIG. 4 (1). This is because the pixel has a saturation value range, but the region 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 details of step 110. In step 162, one area is selected from the areas extracted in step 108, that is, the routine of FIG. 7, as the area of interest, and the horizontal fillet diameter of the area of interest is selected. And the size of the attention area is standardized by performing the enlargement / reduction processing of the attention area so that the vertical fillet diameter becomes a predetermined value.
The density value or the brightness value is standardized according to the following equation (6).

[0031]

[Equation 4] However, dmax: maximum density value (or brightness value) in the area dmin: minimum density value (or brightness value) in the area ds: full-scale density value (or brightness value) of the image sensor d: density value before normalization (or brightness) Value) dr: post-normalization density value (or brightness value) In step 164, a plurality of types (10 types in this embodiment) of standard face images stored in advance (face image viewed from the front, viewed from the side) The correlation coefficient r of the attention area with respect to the face image (left and right), the downward face image, the upward face image, etc. is calculated by the following (7).
The correlation coefficient is calculated by using an equation and is used as a feature amount. This standard face image, even if only the data of the face outline,
It may be data obtained by adding the data of the internal structure (eyes, nose, mouth, etc.) of the face to the data of the outline of the face.

[0032]

[Equation 5] However,

[0033]

[Equation 6] Where T is the length of the horizontal and vertical fillet diameters of the image (here, the lengths of the fillet diameters are the same), f (x, y) is the region of interest, and g (x, y) is the standard. Represents a face image.

Then, in step 166, it is determined whether or not the region of interest is a human face by linear discriminant analysis using the above-mentioned characteristic amount as a variable, and R, G, B photometric data of the region determined to be a face. Is output to the proper exposure amount calculation circuit 40. In step 168, it is determined whether or not the determination as to whether the face is a face has been completed for all the extracted regions. If not, steps 162 to 168 are repeated.

In the above, the correlation coefficient is used as the feature amount used for determining whether the face is a person's face. However, the invariant derived from the normalized central moment around the center of gravity, which will be described below, Autocorrelation functions or geometric invariants may be used.

The central moment μ _pq about the (p + q) th centroid of the image f (x, y) is

[0037]

[Equation 7] However,

[0038]

[Equation 8] Then, the normalized central moment around the center of gravity is

[0039]

[Equation 9] However, y = (p + q + 2) / 2 p + q = 2,3, ... From the above, the following seven invariants ψ _i, (i
= 1, 2, ..., 7) is derived.

[0040]

[Equation 10] Moreover, the autocorrelation function R _f is expressed as follows.

[0041]

[Equation 11] Then, the geometric invariant feature amount is represented by the following equation.

[0042]

[Equation 12]FIG. 9 shows a proper exposure amount calculation routine by the proper exposure amount calculation circuit 40.
This is an example of a face extraction,
R of one face area extracted as described above by the output circuit 36,
The G, B three-color photometric data is read, and the next step 172
Face read based on equation (8) below
Area average density (hereinafter, average face density) FD _iCalculate
It The calculation result is stored in a memory (not shown). next
In step 174, the entire face area is read from the image frame.
It is determined whether or not there is any, and if there is any remaining, step 170,
Repeat 172. When reading of all face areas is completed,
In step 176, it is determined whether there are a plurality of extracted face areas.
to decide. In the negative judgment, when there is only one extraction area,
Proceed to step 186.

FD _i = (FD _iR + FD _iG + FD _iB ) / 3 −−− (8) where i = 1, ... N (number of face area) FD _iR , FD _iG , FD _iB : R in i area , G, B3
Average density for each color.

If there are a plurality of extracted face areas, an affirmative decision is made in step 176, and in step 178 the maximum density FDmax and the minimum density FDmin of the average face density FD _i for each face area are obtained, and this difference (FDmax- FD
min) is greater than or equal to a predetermined value Dthd. If the difference is less than the predetermined value and a negative determination is made, there is little variation in face density, so the average obtained by simply averaging a plurality of average face densities in step 180 is set as the face area average density FD of this image frame in step 186. Go to. In addition, step 1
One average face density may be selected and output without obtaining the average of a plurality of average face densities in 80.

On the other hand, the maximum density FDmax and the minimum density FD
If the difference in min is greater than or equal to the predetermined value Dthd, step 18
Go to 2. In step 182, the extracted face areas are classified into two groups based on the average face density, and the variance between the classified groups (hereinafter, inter-group variance) σ _A and the average within each group. The density value FDx of the divided part where the ratio (hereinafter, dispersion ratio) rt with the variance of face density (hereinafter, intra-group dispersion) σ _B is maximized is obtained. That is, as shown in FIG.
A histogram of the density values and the number of people when one extracted face area is taken as one is obtained by using a coordinate system composed of the density value axis and the people axis that are orthogonal to each other. In this histogram, a predetermined density value Dx is used as a density classification reference value, and a first group including a face area having an average face density FD _i less than the density value Dx and a face having an average face density FD _i greater than or equal to the density value Dx It is classified into a second group including a region. The dispersion ratio rt is obtained for this classified group based on the following equation (9). next,
The density value Dx at which the dispersion ratio rt is maximized is obtained by sequentially increasing or decreasing the density value Dx, and the obtained value Dx is set as the density value FDx of the divided portion. This dispersion ratio rt is maximized because the optimum density value F in which each mountain in the histogram of FIG. 10 is divided into single peaks with the density value FDx as a boundary.
Dx will be selected.

Rt = σ _A ² / σ _B ² ----- (9) Here, the inter-group dispersion σ _A and the intra-group dispersion σ _B can be obtained by the following formula (10).

Σ _A = {n ₁ (FD ₁ m-FDm) ² + n ₂ (FD ₂ m-FDm) ² } / n and σ _B = σ _T ² −σ _A ² −−−− (10) , Np: the number of persons included in the p-th group (n ₁ , n ₂ ) FDpm: average density value (FD of the face area included in the p-th group
₁ m, FD ₂ m) FDm: average density value of all extracted face areas n: number of extracted face areas σ _T : total variance Further, the above total variance can be obtained by the following equation (11). it can.

Σ _T = ΣΣ (FDpk−FDm) ² / n ----- (11) where FDpk: average face density of the kth face area of the p-th group.

At the next step 184, the first group is included.
Average face density FD ₁m for this image frame
Then, the process proceeds to step 186 as the face area average density FD. This
Average density value FD of the first group₁m is the average face area density of the image frame
The degree FD is for races sensitive to color changes in the face area.
Only for the following exposure amount correction
It That is, when the reflectance of the face is low, the reflectance of the face is
When there is some light and dark in the image obtained compared to when it is high
Even if it is a combination, it is affected by the high density formed in the face area
Less is. Therefore, as described above, the
By dividing the density of several face regions into two groups,
Face area in the first group with high emissivity and the second group with low face reflectance
Are classified, and the reflectance of the face is selected by selecting the first group.
Based on the density value output excluding the density of the face area on the low side
It can be corrected based on.

In step 186, the calculated average face density FD and the average image density D _i of one frame calculated by the average density calculation circuit 38 (i = one of R, G, and B) are used. The appropriate exposure amount E _i is calculated according to the following equation so that the average face density becomes the reference density, and is 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 −−− (12) 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 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, which is determined according to the type of printing lens.

MB: A correction value (master balance value) for variations in the print light source and changes in the 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.

[0060]

[Equation 13] Here, K _a and K _b are constants, and FD is the face area average density obtained above.

Further, the density correction amount K ₁ in the above equation (12) 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.

[0062]

[Equation 14] However, K _c is a constant.

Further, the density correction amount K ₁ of the above equation (12),
The color correction amount K ₂ is used as the correction amount obtained by the film inspection device, and the average density D of the print frame of the formula (12) is used.
_The exposure amount may be obtained by replacing _i with the average density FD _i of the face area.

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 the present embodiment, the density of a plurality of face areas included in the original image is determined from the extracted face area data, and the first group having high face reflectance and the first group having low face reflectance are used. Since the exposure amount is corrected according to the face density of the first group, which is classified into two groups and the face reflectance is high, even when there are a plurality of face areas of different races in the original image, The exposure amount can be determined so that each face density becomes an appropriate density without being affected by the face density on the side where the face reflectance is low.

In this embodiment, the case where the density is measured from the image on the negative film has been described, but a reversal film may be used.

FIG. 11 shows a modification in which the present invention is applied to an exposure amount determining device separate from the printer or printer processor. Note that, in FIG. 11, portions corresponding to those in FIG. 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. 12, the face extraction circuit of FIG. 11 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. 13, 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, and 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,

[0070]

[Equation 15] Double speed is possible.

This parallel processing device can also be applied to the printer of FIG. The present invention, besides determining the exposure amount of a photo printing apparatus, determines the exposure amount of a digital color printer, determines the copying conditions of a copying machine, determines the exposure amount of a camera, determines the display conditions of a CRT screen, and creates a hard copy from magnetic image data. It can also be applied to the determination of the amount of light.

[0072]

As described above, according to the present invention, since the exposure amount is determined based on the densities of the density groups classified into the similar density groups based on the density of the face area, the original image is determined. Even if there are a plurality of face areas having different reflectances, the obtained density is not biased and the optimum density can be obtained for each face area.

[Brief description of drawings]

FIG. 1 is a schematic diagram showing a printer according to a first 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 showing an appropriate exposure amount calculation routine of an appropriate exposure amount calculation circuit.

FIG. 10 is a diagram showing a histogram of density values and the number of people.

FIG. 11 is a schematic diagram of an exposure amount calculation device to which the present invention is applied.

FIG. 12 is a schematic diagram of an exposure amount calculation device that performs parallel processing by a plurality of face extraction circuits.

FIG. 13 is a diagram showing a time chart of parallel processing.

FIG. 14 is a diagram showing the spectral reflectance characteristics of human skin.

[Explanation of symbols]

 28 CCD image sensor 30 amplifier 36 face extraction circuit 40 proper exposure amount calculation 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 on the color original image based on the data obtained by photometry. A color area having the same or similar hue or hue and saturation is obtained, and it is determined whether or not the obtained color area is a human face area, and the density of the area determined to be the human face area is obtained and obtained. When the difference between the maximum value and the minimum value of the densities exceeds a predetermined value, the area of the face of the person is classified into similar density groups based on the calculated densities, and at least one of the classified density groups is selected. An exposure amount determining method for determining an exposure amount to a copy material based on the selected density group. 2. The exposure amount to the copy material is determined so that the density of a density group including a face region having a face of a person who is an object is a predetermined value or more becomes a reference density. Item 2. The exposure amount determination method according to Item 1. 3. 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, at least one of the outline and internal structure of each divided area is judged to judge whether or not it is a human face, and the density of the area judged to be the human face is calculated. When the difference between the maximum value and the minimum value of is greater than a predetermined value, the face area of the person is classified into similar density groups based on the calculated density, and at least one of the classified density groups is selected. The exposure amount to the copy material based on the density group selected Exposure amount determining how to. 4. 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 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 group, and the group corresponding to the divided mountain In addition, the original color image was divided for each group, and at least one of the contour and internal structure of each divided area was judged to judge whether it was a human face. If the difference between the maximum value and the minimum value of the calculated density exceeds a predetermined value, the area of the person's face is classified into similar density groups based on the calculated density, and is classified. Select at least one of the selected concentration groups Exposure amount determining method for determining the exposure amount of the copy material Zui.