Abstract: An extraction-pattern storing unit stores therein information related to a plurality of different extraction patterns for extracting a predetermined number of pixels from pixels surrounding a pixel that is a target for detecting a face part image. A face-part-image detecting unit extracts a pixel using the different extraction patterns stored in the extraction-pattern storing unit, and detects the face part image included in an image using a feature amount of an extracted pixel. A face-image detecting unit detects a face image from the image based on the face part image detected by the face-part-image detecting unit.

Abstract: An average face-image is created from images belonging to a face-image group and sample points are specified on the average face-image. Sample points are detected on images in the face-image group corresponding to sample points on the average face-image thereby creating recognition subspaces of feature vectors. Sample points are detected on registered images and an input image corresponding to the sample points on the average face-image by using the recognition subspaces. A degree of similarity between the feature vectors in the recognition subspaces of the registered images and the input image is calculated at the detected sample points. Finally, a person is verified based on the degree of similarity.

Abstract: An extraction-pattern storing unit stores therein information related to a plurality of different extraction patterns for extracting a predetermined number of pixels from pixels surrounding a pixel that is a target for detecting a face part image. A face-part-image detecting unit extracts a pixel using the different extraction patterns stored in the extraction-pattern storing unit, and detects the face part image included in an image using a feature amount of an extracted pixel. A face-image detecting unit detects a face image from the image based on the face part image detected by the face-part-image detecting unit.

Abstract: An average face-image is created from images belonging to a face-image group and sample points are specified on the average face-image. Sample points are detected on images in the face-image group corresponding to sample points on the average face-image thereby creating recognition subspaces of feature vectors. Sample points are detected on registered images and an input image corresponding to the sample points on the average face-image by using the recognition subspaces. A degree of similarity between the feature vectors in the recognition subspaces of the registered images and the input image is calculated at the detected sample points. Finally, a person is verified based on the degree of similarity.

Abstract: Cotton-polyester fiber-blended textile fabrics and knitted goods are resin-finished to be imparted with a high soft feeling and a durable crease resistance using a resin bath containing a specific combination of an amino resin having in the molecule at least two N-methylol groups which may be partially or wholly alkylated, a diol type surface active agent and an acid catalyst for cross-linkage.