Matsushita Electric Industrial Co., Ltd.

Masaki Souma

Kenji Nagao

Shin Yamada

Ryuta Itoh

In an apparatus for detecting a human face from an input image, candidate areas for an eye and candidate areas for an interval between eyes are detected from the image, an area of the interval between eyes is determined from the positional relationship between the candidate areas for an eye and candidate area for an interval between eyes, and a face area is determined so that the face area includes the area of the interval between eyes.

Method for detecting a human face and an apparatus of the same

A feature extraction and pattern collation system for statistically analyzing a set of samples of feature vectors to calculate a feature being an index for a pattern identification, which is capable of identifying confusing data with a high robustness. In this system, a storage section stores a feature vector inputted through an input section and a neighborhood vector selection section selects a specific feature vector from the feature vectors existing in the storage section. The specific feature is a neighborhood vector close in distance to the feature vector stored in the storage section. Further, the system is equipped with a feature vector space production section for outputting a partial vector space. The partial vector space is made to maximize the local scattering property of the feature vector when the feature vector is orthogonally projected to that space. The pattern collation system is thus capable of obtaining learning data from a plurlaity of different persons.

Feature extraction system and face image recognition system

A system capable of pattern matching between a less noisy face image and a noisier face image obtained in different imaging conditions. A weighted sum of a covariance matrix calculated from less noisy sample images obtained for a plurality of sample faces and a covariance matrix calculated from differences between the less noisy sample images and corresponding noisier sample images obtained for the sample faces is first obtained. 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. In actual pattern matching, a first face image and a second face image are first obtained. The degree of similarity between the first and second face images is calculated based on a conventional formula by using the result of the principal component analysis. The matching between the first and second face images is achieved based on a comparison of the degree of similarity with a predetermined threshold value.

Face recognition-matching system effective to images obtained in different imaging conditions

A feature extraction system for statistically analyzing a set of samples of feature vectors to calculate a feature being an index for a pattern identification, which is capable of identifying confusing data with a high robustness. In this system, a storage section stores a feature vector inputted through an input section and a neighborhood vector selection section selects a specific feature vector from the feature vectors existing in the storage section. The specific feature is a neighborhood vector close in distance to the feature vector stored in the storage section. Further, the system is equipped with a feature vector space production section for outputting a partial vector space. The partial vector space is made to maximize the local scattering property of the feature vector when the feature vector is orthogonally projected to that space.

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Yokohama, Japan

Masaki Souma