JP2695067B2 - Method of extracting data of human face and method of determining exposure amount - Google Patents

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 of a person's face and a method for determining an exposure amount, and more particularly, to a method for copying a color original image onto a color copying material or a black and white copying material. The present invention relates to a method of extracting density data of a face and a method of determining an exposure amount using the method.

[0002]

2. Description of the Related Art The most noticeable part when viewing a photograph of a person is the face of the person. To create a high-quality photograph, the color of the face of the person must be adjusted appropriately. It is necessary to bake to a different color.

Conventionally, a face area in an original image of a color film is designated with a light pen to extract density data of a person's face, and the face color is appropriately printed based on the extracted density data. Exposure is determined. Such a technique is disclosed in JP-A-62-115430, JP-A-62-115431, and JP-A-62-1154.
No. 32, JP-A-62-189456, JP-A-62-189457, JP-A-63-138340
And JP-A-63-178222.

[0004] However, in the above-mentioned conventional technique, there is a problem that the printing operation takes a long time because the operator has to specify a face area with a light pen for each image. Further, since the operator must visually specify the face area, it is difficult to perform unmanned operation.

Further, Japanese Patent Application Laid-Open No. 52-156624,
JP-A-52-156625, JP-A-53-123
No. 30, JP-A-53-145620, JP-A-53-145621, JP-A-53-145622
The following publication describes a method of extracting human face data by extracting skin color data. That is, the color original image is divided into a large number of photometry points, and each photometry point is separated into three colors of R (red), G (green), and B (blue), and photometry is performed. Is determined to be within the skin color range. Then, clusters (groups) of photometric points determined to be in the skin color range are used as face density data. However, in this method, since the color within the skin color range is assumed to be the density data of the face, parts other than the face having the skin color or the color similar to the skin color, such as the ground, a tree trunk, and clothes, are also included as the face density data. It will be extracted. Further, even when the same subject is photographed under the same conditions, the color of the photographed image differs depending on the film type, so that if the film type differs, the face density data may not be automatically extracted. Furthermore, if the color of the light source that illuminates the subject differs, the tint of the captured image differs (for example, an image captured using a fluorescent lamp as a light source becomes green). May not be extracted automatically.

In order to solve the above-mentioned problem caused by different light source colors, photometric data in a skin color range may be extracted after performing light source color correction. As a light source,
Sunlight, fluorescent light, and tungsten light can be broadly classified. The color of sunlight varies depending on the season and time zone, and the color varies depending on whether the light is direct light or indirect light even in the same season and time zone. In addition, artificial lights such as fluorescent lamps have various colors with the diversification of products. Therefore, it is difficult to perform light source correction by specifying the light source type for each light source. Further, even if the light source correction can be completely performed, it is not possible to prevent extraction of a skin color or a portion similar to the skin color such as the ground or a tree trunk, and to cope with a case where the film type is different. Can not.

The present invention has been made to solve the above-mentioned problems, and it is possible to automatically extract 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 data extraction method and an exposure amount determination method using this method.

[0008]

According to a first aspect of the present invention, a color original image is divided into a large number of pixels, and each pixel is separated into three colors of red light, green light and blue light. Photometry, obtain a hue value histogram based on the data obtained by the photometry, divide the obtained histogram into peaks,
It is determined which of the divided mountains the respective pixels of the color original image belong to, and the pixels are divided into groups corresponding to the divided mountains, and the color original image is divided for each group and At least one of the contour and the internal structure of the area is determined to determine whether or not it is a person's face, and data of the area 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 hue and saturation values, 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 divide the pixel. While dividing into groups corresponding to the 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 data of the area determined to be the human face is obtained. Extract.

Then, the invention of claim 3 determines the exposure amount to the copying material based on the density data of the face of the person extracted as described above.

[0011]

According to the first aspect of the present invention, a color original image is divided into a large number of pixels, and each pixel is decomposed into three colors of red light, green light and blue light, photometry is performed, and a hue is obtained based on data obtained by photometry. Find a histogram of the values. Next, the obtained histogram is divided into peaks at a valley or a foot of a peak of the histogram as a boundary. Thus, the hue value range of each mountain is determined. Next, it is determined which hue value range the hue value of each pixel belongs to, so that it is determined which of the crests each pixel belongs to. Cluster). Subsequently, the color original image is divided into regions 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 the contour of the human face and the contours of other parts, the internal structure of the human face and other Since it is clearly different from the internal structure of the part, it is possible to determine whether it is a human face by determining at least one of the contour and internal structure of each region. It is possible to extract the face data of the person by extracting.

If there is a change in the type of film or light source, a change with time, a difference in film development, etc., the color of the original color image changes uniformly over the entire screen. Since the group formed by each pixel of the image is preserved only by changing the position of, the divided area of the color original image does not change even if the color changes. Therefore, in the present invention,
Even if the color or color range of a color original image changes due to a change in a film type or a light source type, a change over time, a film development difference, or the like, density data of a human face can be extracted.

When the hue of the person's face, which is a characteristic part 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 values, the face of the person and other It may be difficult to distinguish it from the part. Therefore, in the invention of claim 2, the saturation value is further introduced in addition to the hue value to obtain a two-dimensional histogram of the hue value and the saturation value,
The two-dimensional histogram is divided for each mountain and the color original image is divided in the same manner as described above, and at least one of the contour and the internal structure of the divided region is judged to extract the face data of the person.

In the present invention, since the hue value and the saturation value are used, the data of the human face is mixed even if the human face and the portion having the same or similar hue (for example, the ground, trees, etc.) are mixed. Can be extracted. That is, although the hue of a person's face is similar to the skin-colored portion of the ground, trees, etc., in most cases, the saturation is different, so that the human's face's hue is based on the two-dimensional histogram of the hue value and the saturation value. By extracting the data, it is possible to extract the face data of a person from an image in which a face, the ground, trees, and the like are mixed.

[0015]

Embodiments of the present invention will be described below 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 automatic printer according to the present embodiment includes a transport roller 12 that transports a color negative film 10. A light source 1 is provided below the color negative film 10 transported by the transport rollers 12.
4. A color correction filter 16 such as a dimming filter and a diffusion box 18 are sequentially arranged. Above the negative film 10, the light transmitted through the negative film 10 is
A distribution prism 20 for distributing in the direction is arranged.
On one optical path distributed by the distribution prism 20, a projection optical system 22, a black shutter 23, and color paper (printing paper) 24 are sequentially arranged, and on the other optical path, a projection optical system 26 and a CCD image sensor. 28 are arranged in order. This CCD image sensor 28
The entire 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
Photometry is performed by separating the color into three colors (red), G (green), and B (blue). The CCD image sensor 28 is connected to a 3 × 3 matrix circuit 34 for correcting the sensitivity of the CCD image sensor via an amplifier 30 for amplifying 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 storing a program of a routine described below, and calculates the average density of the entire screen. It is connected to an appropriate exposure calculation circuit 40 via an average density calculation circuit 38 for calculating. The appropriate exposure amount calculation circuit 40 is connected to the color correction filter 16 via a driver 42 for driving a color correction filter.

Next, the operation of this embodiment will be described. Light source 14
Are transmitted through the color correction filter 16, the diffusion box 18, and the color negative film 10, are distributed by the distribution prism 20, and are received by the CCD image sensor 28 via the projection optical system 26. At this time, the black shirt 23 is closed. By receiving the light, the CCD image sensor 28 divides the entire screen into a large number of pixels, decomposes each pixel into three colors of R, G, and B, performs photometry, and outputs a photometric data signal. The photometric data signal is amplified by an amplifier 30 and then converted into a digital signal by an A / D converter 32. The sensitivity of the image sensor is corrected by a 3 × 3 matrix circuit 34, and a face extraction circuit 36 and an average density calculation circuit 38 Is input to This average density calculation circuit 3
In step 8, the average density of one entire screen is calculated. The face extraction circuit 36 estimates the portion of the face of the person in one screen as described below, and outputs R, G, and B colorimetric data of the portion estimated as the face. The exposure calculation circuit 40 calculates the exposure 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 outputs the color correction filter 16 via the driver 42. Is controlled, and the black shutter 23 is opened and closed to perform printing. When the average density calculated by the average density calculation circuit 38 is used, an exposure correction amount for the average density can be calculated. If the exposure correction amount is not determined, the exposure amount may be determined directly from the three-color photometric data output from the face extraction circuit 36 without necessarily requiring the average density calculation circuit 38.

FIG. 2 shows a face extraction routine by the face extraction circuit 36. In step 100, noise removal, that is, smoothing of the three-color photometry data input is performed. In the next step 102, the R, G, and B colorimetric data are converted to H (hue value) and L by the following equations (1) to (3).
(Lightness 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 such that the minimum value is 0 and the maximum value is 1 as shown in the three-dimensional color coordinates of FIG.
Colorimetric data, min () is the minimum value in parentheses, r ′, g ′, b ′ are r ′ = R / L, g ′ = G / L,
b ′ = B / L. H ′ is given by the following equation (4), and Pi (i is one of R, G, and B) is P
It 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 the orthogonal hue value axis, saturation value axis, and pixel number axis. Then, in the next step 106, as described later, the obtained two-dimensional histogram is divided for each mountain, that is, clustering of the two-dimensional histogram is performed. In the next step 108, the clustered 2
A large number of pixels are clustered based on the peaks of the dimensional histogram, the screen is divided based on the clustering, and a region that is a candidate for a human face is extracted from the divided region. In the next step 110, a face area is estimated from the area extracted as a face candidate, and R, G, B three-color photometric data of the area estimated as the face is output. Then, it is determined in step 112 whether or not printing of all frames has been completed. When it is determined that printing has been completed, this routine is terminated.

Next, the details of steps 106 to 110 will be described. FIG. 5 shows 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 an evaluation area for simplifying the explanation. In step 122, it is determined whether or not there is an evaluation area. When the evaluation area cannot be cut out in step 120, that is, when the evaluation of all the areas is completed, there is no evaluation area, so this routine ends. If there is an evaluation region, the X and Y axes for creating a histogram for mountain cutting are determined in step 124. In other words, the evaluation area is rotated around an axis parallel to the pixel number axis, and when a mountain of the histogram is viewed from the side, a multimodal property is prioritized and a position where the mountain is the sharpest is obtained. Determine X and Y axes. If the processing time needs to be shortened, the accuracy will be slightly 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, a one-dimensional histogram is created by projecting the two-dimensional histogram on the X and Y axes. In the example of FIG. 4A, when viewed from a direction orthogonal to the X axis, the peaks denoted by reference numerals 1 and 2 appear to overlap each other. Three peaks, that is, peaks 1 and 2, and peaks 4 appear, and when viewed from a direction orthogonal to the Y axis, the peaks 1 to 4 appear to be overlapped, so that one dimension about the Y axis is obtained. One peak 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 peaks are cut out from the histogram on the X axis based on the evaluation function.

[0025]

(Equation 3)

Here, 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 (the existence range of the valleys and the skirts) of the average value T of the evaluation function H (a) is obtained. Next, the minimum position of the histogram within this range is defined as a valley or a skirt of the histogram. Then, a histogram is cut out at the determined valley or foot.

The above-mentioned cut-out 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 in which the evaluation function H (a) is equal to or less than the average value T for the negative part is a range in which the feature amounts are v0 to v1 and v2 to v3. The position where the frequency of the histogram within this range is the minimum is the range v
Av0 = v0 for 0 to v1, av1 for the range v2 to v3
Av0 is obtained as a skirt, and av2 is obtained as a valley. A histogram is cut out at this position.

In step 130, the peak of the histogram on the Y axis is cut out in the same manner as the peak on the X axis. Next step 132
Then, a region where the peaks of the one-dimensional histogram on the X-axis and the Y-axis cut out as described above are overlapped on the two-dimensional histogram is obtained, and the peak is cut out from the two-dimensional histogram on the hue value and the saturation value. Region E in FIG. 4 (1)
Reference numeral 1 denotes an example of a mountain cut out as described above.

In the next step 134, it is determined whether or not the mountain cut out from the two-dimensional histogram is a single peak. If not, steps 124 to 134 are performed until the mountain cut out from the two-dimensional histogram becomes a single peak. repeat. The area E2 in FIG. 4 (3) shows an example of a single-peak mountain cut out as described above.

In the next step 136, processing (labeling) for attaching a label for identifying the cut-out single-peak mountain is performed. 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 a single peak.

FIG. 7 shows the details of step 108 in FIG. 2. In step 140, the X-axis range XR (FIG. 4 (3)) and Y 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 the pixel clustering is performed. At the same time, 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 region on the original image. Numbering is performed on the divided areas. FIG. 4 (2) shows an example in which the original image is divided, and the pixels of the respective regions denoted by reference numerals 1 to 4 are included in the single-peaked mountain denoted by reference numerals 1 to 4 in FIG. Corresponding to the pixel. In FIG. 4 (1), pixels belonging to the same single-peak mountain are divided into different regions in FIG. 4 (2). This is because in FIG. Although the pixel has a saturation value range, the region is divided in FIG. 4B.

[0033] In the next step 142, the infinitesimal area is removed by determining the area of the divided areas, re-numbering. In the next step 144, a large area is obtained by performing a contraction process of deleting all the boundary pixels of the region and removing it by one skin, and a dilation process of expanding the boundary pixels in the direction of the background pixel and increasing the thickness of one skin in contrast to the contraction process. Are separated from the large area. Performs renumbering by removing the following infinitesimal area as in step 146 In step 142, in order to separate the region between that weak binding at step 148, performs the same shrinkage as described above, the expansion process, the removal and renumbering the same manner as described above infinitesimal area at step 150.

FIG. 8 shows the details of step 110. In step 162, one region is selected from the regions extracted in step 108, that is, the routine of FIG. 7, as the region of interest, and the horizontal fillet diameter of the region 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 luminance value is normalized according to the following equation (6).

[0035]

(Equation 4)

Where 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 luminance value) dr: post-normalization density value (or luminance value) In step 164, a plurality of types (10 types in the present embodiment) of standard face images stored in advance (face image viewed from the front, horizontal direction) 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. viewed from
This correlation coefficient is used as a feature value. Even if this standard face image is data of only the face outline,
The data may be data obtained by adding data of the internal structure of the face (eye, nose, mouth, etc.) to the data of the contour of the face.

[0037]

(Equation 5)

However,

[0039]

(Equation 6)

Where T is the horizontal and vertical fillet diameters of the image (here, the fillet diameters are the same), f
(X, y) represents a region of interest, and g (x, y) represents a standard 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 or not the face is a face has been completed for all the extracted areas. 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, an 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 around the (p + q) th centroid of the image f (x, y) is

[0044]

(Equation 7)

However,

[0046]

(Equation 8)

Then, the normalized central moment around the center of gravity is as follows.

[0048]

(Equation 9)

However, from the above, y = (p + q + 2) / 2 p + q = 2, 3, ... From the normalized central moments around the second and third centroids, the following seven invariants ψ _{i ,} (I
= 1, 2,..., 7) are derived.

[0050]

(Equation 10)

Further, the autocorrelation function R _f is expressed as follows.

[0052]

[Equation 11]

The geometric invariant feature amount is expressed by the following equation.

[0054]

(Equation 12)

The appropriate exposure amount calculation circuit 40 is composed of the face extraction circuit 3
6, the R, G, and B photometric data of the face area extracted as described above and the screen average density D _i (i = R, G, or B) of one frame calculated by the average density calculation circuit 38 ) and using the calculated the proper exposure amount E _i according to the following equation, and outputs to the driver 42. The driver 42 controls the light adjustment filter 16 calculates 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 (8) 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 frame 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 standard color paper, which is determined according to the type of color paper.

LB: For standard print lens. The correction lens balance value is determined according to the type of the 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 equation.

[0065]

(Equation 13)

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 above equation (8) may be used as the correction value obtained by the film inspection apparatus, and the color correction amount K ₂ may be expressed by using the face area average density as follows.

[0068]

[Equation 14]

However, K _c is a constant.

Further, the density correction amount K ₁ and the color correction amount K ₂ in the above equation (8) 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 (8) is calculated for the face area. The exposure amount may be obtained by replacing the average density FD _i .

In this embodiment, since the judgment is made by using the contour of the area and the internal structure, it is possible to extract the face data from the image in which the faces, the ground, the trees and the like having similar hues are mixed. .

FIG. 9 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 addition, in FIG. 9, portions corresponding to those in FIG. Although the average density calculation circuit 38 is not always necessary, an integrated transmission density detection circuit for detecting LATD of the entire screen may be used instead.

In FIG. 10, the face extraction circuit of FIG. 9 is composed of a plurality of face extraction circuits 36 ₁ , 36 _2, ... 36 n, 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. 11, calculates the exposure amount, and outputs the result. In FIG. 11, t ₁ is the image reading time for one frame, t _1
2 one frame of an exposure amount calculating time, t ₃ is the one frame of an exposure amount computation result transfer time is t ₂ >> t _1, t _3. The face extraction circuit 36 ₁ reads an image of one frame at time t ₁ ,
It calculates the exposure amount in ₂ hours, and transfers the calculation result t ₃ hours. At the same time when the image reading of one frame by the face extraction circuit 36 ₁ is completed, the film is fed by one frame and the face extraction circuit 36
Image reading of one frame by ₂ is started, the face extraction circuit 3
6 ₁ of the image reading of the exposure operation and the face extraction circuit 36 ₂ is performed in parallel, and so the face extraction circuit 36 _3, 36 ₄
.. 36n are 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 the parallel processing is not performed
Is Ts = m · n (t ₁ + t ₂ + t ₃ ). Therefore,

[0075]

(Equation 15)

It is possible to double the speed.

This parallel processing device can also be applied to the printer of FIG. According to the present invention, in addition to the determination of the exposure of the photographic printing apparatus, the determination of the exposure of the digital color printer, the determination of the copying conditions of the copying machine, the determination of the exposure of the camera, the determination of the display conditions of the CRT screen, and the creation of a hard copy from the magnetic image data It can also be applied to the determination of the amount of light at the time.

[0078]

As described above, according to the present invention, at least one of the contour and the internal structure of the divided area is judged based on the histogram of the hue value to judge whether it is a person's face or not. Therefore, it is possible to accurately extract human face data even if the tint or color range of the color original image changes due to changes in film type or light source type, changes in film characteristics over time, film development differences, etc. The effect is obtained.

Further, since at least one of the contour and the internal structure of the divided area is judged based on the two-dimensional histogram of the hue value and the saturation value, it is judged whether or not it is the person's face. Data of a person's face can be extracted even if parts having the same or similar hues as the face are mixed.
The effect is obtained.

[Brief description of the drawings]

FIG. 1 is a schematic view 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. 4A is a diagram illustrating 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 where a single peak is cut out from a two-dimensional histogram.

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

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

FIG. 7 is a diagram showing details of step 108 in FIG. 2;

FIG. 8 is a diagram showing details of step 110 in FIG. 2;

FIG. 9 is a schematic diagram of an exposure amount calculation device to which the present invention is applied.

FIG. 10 is a schematic diagram of an exposure amount calculation device that performs parallel processing by a plurality of face extraction circuits.

FIG. 11 is a diagram showing a time chart of parallel processing.

[Explanation of symbols]

 28 CCD image sensor 30 Amplifier 36 Face extraction circuit

Claims (3)

(57) [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, A color original image is divided, and at least one of the contour and internal structure of each divided area is determined to determine whether it is a person's face, and the data of the area determined to be the person's face is extracted. Face data extraction method. 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. The original color image was divided into groups, and at least one of the outline and internal structure of each divided area was judged to determine whether it was a human face. A method for extracting human face data that extracts region data. 3. 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 or 2 .