Image analysis – Applications – Personnel identification
Reexamination Certificate
1997-12-03
2002-02-05
Couso, Jose L. (Department: 2721)
Image analysis
Applications
Personnel identification
Reexamination Certificate
active
06345109
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to a pattern recognition system and, more specifically, to a system which permits a reliable identification of a subject image of an object, e.g., a human face even when noise or variation components have been added to either of the subject image and the reference images.
2. Description of the Prior Art
Various pattern recognition schemes have been proposed based on the principal component analysis (or Karhunen-Loeve expansion). One such scheme is disclosed in U.S. Pat. No. 5,164,992, “Face Recognition System” issued to M. A. Turk et al. on Nov. 17, 1992. According to this scheme, a training set of N
2
-dimensional image vectors is reduced by a principal component analysis into a set of M orthonoral eigenvectors u
k
(k=1, 2, . . . M) and their associated eigenvalues &lgr;
k
, which best describes the distribution of the images. Then, the vectors of a subject image and reference images (a super set of the training images) are projected onto the u
k
-vector space. The subject is identified based on a distance between the projected subject and each of the projected reference images, which is calculated according to one of well-known distance definitions, e.g., the Euclidean distance.
N. Costen, I. Craw and S. Akamastu, in “Automatic Face Recognition: What Representation?”
Technical Report of IEICE
(
The Institute of Electronics, Information and Communication Engineers
)
of Japan
, HIP95-32 (January 1996), have reported that the recognition accuracy is raised by using the Maharanobis distance.
A modified Maharanobis distance in which a bias value is added to each eigenvalue is reported by N. Kato, M. Abe and Y. Nemoto in “A Handwritten Character Recognition System Using Modified Maharanobis distance”, the
Transaction of IEICE
, Vol. J79-D-II, No. 1, January 1996, at pages 45-52.
It has been proved that the above mentioned recognition schemes provide satisfactory recognition accuracy as long as noise or variation components of either the subject or the reference images are negligibly small. However, in the actual situation, if for example the illumination condition differs vary much at the time of acquiring a subject image and that of a corresponding reference image, this causes the subject and the corresponding reference image to differ significantly from each other, resulting in a failure in the identification of subject in the conventional systems. This is because the effect of the noise components which are included in the reference set of images are not taken into account in any of the conventional systems.
SUMMARY OF THE INVENTION
It is therefore an object of the invention to provide a pattern recognition system and method for permitting a robust pattern recognition of a subject even if large noise components exist in either the subject or reference images.
According to the present invention, a (off line) preprocessing is performed prior to an actual pattern recognition operation so as to enable pattern matching between a first face image having been obtained in a first imaging condition and a second face image having been obtained in a second imaging condition inferior to the first imaging condition. For a plurality of sample faces, respective first sample images are obtained in a first sample imaging condition similar to or the same as the first imaging condition. For the same sample faces, respective second sample images are obtained in a second sample imaging condition similar to or the same as the second imaging condition. A first covariance matrix calculated from the first sample images and a second covariance matrix calculated from differences between the first sample images and the second sample images are weighted and summed into a weighted sum. Then, a principal component analysis is applied to the weighted sum to find and store in memory a result with which principal components are calculated from a given face image.
The result comprises, for example, eigenvectors and corresponding eigenvalues resulted from solving an eigenvalue problem with respect to the weighted sum. Alternatively, the result may comprise a characterizing matrix which, when applied to a given face image vector, yields a principal component vector for the given face image vector.
In actual pattern matching, the first face image and the second face image are obtained through a communication interface, a video camera, and/or an image scanner. The degree of similarity between the first and second face images is calculated on the basis of a standard or well-known formula by using the result of the principal component analysis. It is determined whether the first face image matches the second image, on the basis of a comparison of the degree of similarity with a predetermined threshold value.
According to an embodiment of the invention, there is provided a system which enables pattern matching between a first face image obtained in a first imaging condition and a second face image obtained in a second imaging condition which is noisier than the first imaging condition. The system comprises: means for obtaining, for a plurality (N) of first sample faces, respective first sample images in a sample imaging condition similar to or the same as the first imaging condition; means for obtaining, for the N sample faces, respective second sample images in a second sample imaging condition similar to or the same as the second imaging condition; means for obtaining, for a plurality (L) of second sample faces, respective third sample images in the sample imaging condition; means for obtaining a weighted sum of a covariance matrix calculated from differences between the first sample images and the second sample images and a covariance matrix calculated from the third sample images; means for applying a principal component analysis to the weighted sum to find a result with which principal components are calculated from a given face image; and means for enabling the result to be used for a later pattern matching process executed within the system or by an external system.
In order to enable the system to carry out pattern patching, the system may further comprise: means for obtaining the first face image (e.g., a video or digital camera); means for obtaining the second face image (e.g., a scanner); means operative on the basis of a standard formula for calculating a degree of similarity between the first and second face images by using the data means; and means operative on the basis of a comparison of the degree of similarity with a predetermined threshold value for determining whether the first face image matches the second image.
Alternatively, a system capable of pattern matching between a first face image obtained in a first imaging condition and a second face image obtained in a second imaging condition which is noisier than the first imaging condition may comprise data means stored in a storage for use in extracting principal components from a given face image. The data means is obtained by: taking in, for a plurality of sample faces, respective first sample images in a first sample imaging condition similar to or the same as the first imaging condition; taking in, for the plurality of the sample faces, respective second sample images in a second sample imaging condition similar to or the same as the second imaging condition; calculating a weighted sum of a first covariance matrix calculated from the first sample images and a second covariance matrix calculated from differences between the first sample images and the second sample images; and applying a principal component analysis to the weighted sum to find a result with which principal components are calculated from a given face image. The system further comprises: means for obtaining the first face image; means for obtaining the second face image; means operative on the basis of a standard formula for calculating a degree of similarity between the first and second face images by using the data means; and means operative on the basis of a comparis
Nagao Kenji
Souma Masaki
Cage Shawn B.
Couso Jose L.
Lowe Hauptman Gopstein Gilman & Berner
Matsushita Electric - Industrial Co., Ltd.
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