Image analysis – Applications – Personnel identification
Reexamination Certificate
1999-03-18
2003-09-30
Werner, Brian (Department: 2621)
Image analysis
Applications
Personnel identification
C382S190000, C382S228000, C382S276000
Reexamination Certificate
active
06628811
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to a pattern recognizing method and a pattern recognizing apparatus in which an image or a speech is recognized, a pattern identity judging method and a pattern identity judging apparatus in which it is judged according to the pattern recognizing method whether or not an image or a speech is identical with another image or another speech, a recording medium for recording a software program of the pattern recognition and a recording medium for recording a software program of the pattern identity judging method.
2. Description of the Related Art
In a technical field of a pattern recognition such as a face image recognition or a speech recognition, second order statistics (or covariances) of model patterns are calculated from a set of model patterns registered in a data base in advance, a pattern data space is made from the second order statistics, a distribution of an input pattern in the pattern data space (that is, the portion occupied by an input pattern in the pattern data space) is assumed, and features of the input pattern are extracted to recognize the input pattern.
2.1. Previously Proposed Art:
For example, features of the input pattern are extracted according to a well-known Karhunen-Loeve (KL) expansion method. This feature extraction is, for example, disclosed in a literature “M. Turk, A. Pentland, “Eigenfaces for Recognition”, Journal of Congnitive Neuroscience Volume 3, Number 1, 1991”. Though there are various other methods than the KL expansion method, the other methods are based on the KL expansion method.
In the KL expansion method, each of two patterns Pa and Pb is approximated by a linear combination of basis vectors (the number of vectors is N) Ei (i=1, 2, - - - , N) to produce an approximated pattern, and the collation between the patterns Pa and Pb is performed by using approximated patterns A and B. The approximated patterns A and B are formulated as follows.
A
=
∑
i
=
1
N
αi
Ei
B
=
∑
i
=
1
N
βi
Ei
(
1
)
In the KL expansion method, a covariance matrix is obtained from W pieces of teaching pattern data, an eigenvalue is calculated for each eigenvector of the covariance matrix, N eigenvectors corresponding to N higher eigenvalues (the number N is, for example, 100) are selected as N basis vectors Ei from all eigenvectors of the covariance matrix.
In cases where a pattern data space is defined by the N basis vectors, there are two merits.
(1) The W teaching pattern data projected on each plane defined by two basis vectors are separated from each other to a highest degree. Therefore, the W teaching pattern data can be easily distinguished from each other.
(2) Noises included in the patterns Pa and Pb and changes occurring randomly in the patterns Pa and Pb can be removed.
In the KL expansion method, it is supposed that an assuming precision for distribution parameters calculated from a pattern model set is sufficiently high. For example, in a face image recognition, in cases where a statistic property in a process for obtaining a pattern set agrees with that in a process for obtaining another pattern set, many examinations indicate that a pattern recognition can be performed at a very high precision rate and the collation of the pattern sets can be correctly performed.
2.2. Problems to be Solved by the Invention:
However, in cases where features of a model pattern are extracted according to two types of image receiving processes, there is a case that a first set of teaching pattern data obtained from the model pattern according to the first process greatly differs from a second set of teaching pattern data obtained from the same model pattern according to the second process, so that a statistic property for the first set of teaching pattern data greatly differs from that for the second set of teaching pattern data. For example, in cases where a lighting condition for photographing a first pattern differs from that for photographing a second pattern, there is a case that a statistic property for a first set of pattern data obtained from the first pattern differs from that for a second set of pattern data obtained from the second pattern. As a result, even though features of the first pattern agree with those of the second pattern, because an image recognition for the first and second sets of pattern data is not performed with sufficiently high precision, the collation of the first and second sets of pattern data with each other is not correctly performed, and the identity of the first pattern with the second pattern cannot be judged.
The above problem is based on the supposition that two pattern data sets compared with each other are derived from the common distribution (or the common statistic parameters). Therefore, in cases where two pattern data sets compared with each other are derived from different distributions (or different statistic parameters), the KL expansion method cannot be properly performed in the pattern recognition or the pattern collation.
SUMMARY OF THE INVENTION
A first object of the present invention is to provide, with due consideration to the drawbacks of such conventional pattern recognizing method and apparatus, pattern recognizing method and apparatus in which an input pattern identical with one of referential patterns is recognized with high precision even though a process for obtaining the input pattern of an input sample differs from a process for obtaining the referential patterns from referential samples.
A second object of the present invention is to provide pattern identity judging method and apparatus in which the identity of a first input pattern obtained according to a first process with a second input pattern obtained according to a second process is correctly judged regardless of a statistic property difference between the first and second input patterns occurred according to a difference between the first and second processes.
A third object of the present invention is to provide a recording medium in which a software program of the pattern recognizing method or a software program of the pattern identity judging method is recorded.
The first object is achieved by the provision of a pattern recognizing method, comprising the steps of:
obtaining a set of first teaching patterns of a plurality of teaching samples according to a first pattern obtaining process;
obtaining a set of second teaching patterns of the teaching samples according to a second pattern obtaining process differing from the first pattern obtaining process;
calculating a teaching pattern distribution from the set of first teaching patterns or the set of second teaching patterns;
calculating a teaching distribution of a perturbation between the set of first teaching patterns and the set of second teaching patterns;
calculating a feature extraction matrix, which minimizes an overlapping area between the teaching pattern distribution and the teaching perturbation distribution, from the teaching pattern distribution and the teaching perturbation distribution;
obtaining a set of referential patterns of a plurality of referential samples according to the first pattern obtaining process;
calculating a set of referential feature patterns of the referential samples from the set of referential patterns according to the feature extraction matrix, the set of referential feature patterns being independent of the first pattern obtaining process and the second pattern obtaining process;
receiving an input pattern of an input sample according to the second pattern obtaining process;
calculating an input feature pattern of the input sample from the input pattern according to the feature extraction matrix;
selecting a specific referential feature pattern most similar to the input feature pattern from the set of referential feature patterns; and
recognizing a specific referential sample corresponding to the specific referential feature pattern as the input sample.
The first object is also
Nagao Kenji
Soma Masaki
Matsushita Electric - Industrial Co., Ltd.
Werner Brian
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