Image analysis – Applications – Personnel identification
Reexamination Certificate
1999-05-26
2001-01-30
Mancuso, Joseph (Department: 2723)
Image analysis
Applications
Personnel identification
Reexamination Certificate
active
06181806
ABSTRACT:
FIELD OF THE TECHNOLOGY
The present invention relates to apparatus for identifying persons such as identifying persons by their face images in their admission to a room or searching and outputting the name of a person whose face image resembles an input face image.
BACKGROUND TECHNOLOGY
As a prior apparatus for identifying persons by their face images there is one described in Japanese Pat. laid-open Publication No. Sho 63-177273. The composition of that apparatus is described in the following.
FIG. 15
shows a block diagram showing a composition of the prior apparatus for identifying persons by their face images. In the FIG.,
21
is an image input section that inputs a face image,
22
is a feature-points extracting section comprising an analog-to-digital converter, a central processor, and a memory,
23
is a personal database section that records people's feature points on cards or the like,
24
is a judgment section that uses the feature-points information of above feature-points extracting section
22
and the feature-points information of personal database section
23
to detect their difference and identifies the person having that face image as the person of personal database
23
based on the test whether the difference is greater than a threshold value or not.
The above prior technology extracts, in feature-points extracting section
22
, feature points of a person's face image input from image input section
21
and detects, in judgment section
24
, the differences between these feature points and the feature points of a face image registered in personal database section
23
, and identifies the person based on the amount of the difference. In this case, if the sizes of the two images are different, or if their photographing angles are different, then the differences in the feature points become great, so that even the same person is identified as a different person. In order to avoid such a situation, it was necessary to fix the distance between the image input section and a photographed person and to make him or her face turned straight toward the front. However, this method of inputting a face image imposes a burden on the photographed person, and no matter how hard the person tries to turn straight to the front, a slight rotation and inclination are inevitable, so that it was difficult to obtain an image exactly facing the front.
The basic object of the present invention is to provide an apparatus for identifying persons having high discriminating power.
Another object of the present invention is to provide an apparatus for identifying persons that performs identification with high accuracy against minor fluctuations of photographing conditions such as illumination conditions and the position of the face.
DISCLOSURE OF THE INVENTION
In order to achieve the aforementioned objective, the present invention extracts feature points of face components such as the eyes, nose, mouth, and uses the extracted feature points to divide a face image into small patches of, for example, triangles and describe a face image as the set of these patches. Then a feature amount is extracted for each patch and the set of extracted feature amounts is used to identify the face image of a person. As a feature amount, the average brightness of each patch can be used. In this case, the face image is treated as a monochromatic image, so that the processing can be simplified, and the image formed by obtained patches having graded brightnesses sufficiently reflects the characteristics of the face, that is, concavo-convexity, flatness, curvature and the like are indirectly expressed. Therefore, by using these features, identification of a person can be performed accurately. Besides average brightness, the difference in brightnesses between adjacent patches can be used as a feature amount.
A practical method applies a standard structure model that describes a standard face image as a set of patches. When the feature points of an input face image is extracted, they are compared with the feature points of the standard structure model, and the standard structure model is deformed and adjusted based on the extracted feature point information, to obtain an adjusted structure model corresponding to the input face image. The adjusted structure model obtained in this way is expressed as a set of patches as described above, and a feature amount is extracted for each patch.
The extracted feature amounts are compared with the feature amounts of each person in the personal database to detect the degree of agreement by calculating a distance between the feature amounts. In case of confirming a specific person, the extracted feature amounts are compared with the feature amounts of the specific person. In case of searching for identification, the extracted feature amounts are successively compared with the data stored in the personal database to extract a face image having a large degree of agreement.
The standard structure model may be either 2-dimensional or 3-dimensional. In the 3-dimensional case, if the normal vector of each patch is extracted as well as its brightness, then the concavo-convexity of the face is expressed more clearly, and the position of the light source can be estimated from the distribution of the brightnesses, so that correction depending on the position of the light source can be made possible. Further, if the face is slightly inclined against the camera, errors due to the inclination can be prevented by a correction such that the brightness distribution becomes bilaterally symmetric.
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J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing., pp. 75-83.
S.Y. Guan, B.H. McCormick, Design for a 3D Deformable Brain Atlas, Computer-Based Medical Systems, Proceedings fo the Fourth Annual IEEE Symposium.
Kado Yoshiyasu
Nakagawa Masamichi
Nobori Kunio
Mancuso Joseph
Matsushita Electric - Industrial Co., Ltd.
McDermott & Will & Emery
Patel Jayanti K.
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