Abstract: A learning device according to an embodiment includes an acquirer, an extractor, a plurality of processors, and an identifier. The acquirer is configured to acquire a paper sheet image which is a captured image of a paper sheet. The extractor is configured to extract a plurality of primary feature images having different objects to be recognized from the paper sheet image acquired by the acquirer. The plurality of processors are configured to perform respective convolution and pooling processes on the plurality of primary feature images extracted by the extractor to generate a plurality of secondary feature images having different objects to be recognized. The identifier is configured to sequentially update and learn a parameter set for identifying a sheet type of the paper sheet on the basis of a result of a combination process on each of the plurality of secondary feature images generated by the plurality of processors.

Abstract: An image processing device that acquires an image from an object includes an illuminator having a plurality of light sources, a sensor unit, and a controller. The plurality of light sources irradiates light of respectively different colors onto an object. The sensor unit outputs luminance data for one line obtained by photoelectrically converting light is reflected from the object of that was irradiated by the illuminator onto the object. The controller acquires a random number, and acquires luminance data for one line output by the sensor unit, while causing one light source selected from the plurality of light sources based on the acquired random number to emit light.

Abstract: In a person retrieval apparatus, a plurality of extraction processing sections each extract personal biometric information from images taken by a plurality of cameras. A quality determination section determines a quality of each piece of biometric information extracted by the extraction processing sections. A reliability level setting section sets a reliability level to each piece of biometric information on the basis of the quality determined by the quality determination section. The biometric information extracted by the extraction processing sections and the reliability level set by the reliability level setting section are stored in a memory. In this state, in the person retrieval apparatus, the face retrieval section performs person retrieval processing on each piece of biometric information stored in the memory in descending order of the reliability level corresponding to each piece of biometric information.

Abstract: According to one embodiment, sheet processing includes feature-amount extraction unit configured to extract feature amount from image obtained, standard pattern storage unit configured to prestore standard pattern group for each different soil level, standard pattern group includes standard pattern for each category, selection unit configured to select one standard pattern group from standard pattern groups, similarity calculation unit configured to calculate similarity, based on standard pattern comprised in one standard pattern group selected, and on feature amount extracted, comparison unit configured to compare similarity calculated with preset threshold, determination unit configured to determine category of sheet when similarity is equal to or greater than threshold, as result of comparison, and control unit configured to control selection unit to select one other standard pattern group which has not been selected, when similarity is smaller than threshold, as result of comparison.

Abstract: In a person retrieval apparatus, a plurality of extraction processing sections each extract personal biometric information from images taken by a plurality of cameras. A quality determination section determines a quality of each piece of biometric information extracted by the extraction processing sections. A reliability level setting section sets a reliability level to each piece of biometric information on the basis of the quality determined by the quality determination section. The biometric information extracted by the extraction processing sections and the reliability level set by the reliability level setting section are stored in a memory. In this state, in the person retrieval apparatus, the face retrieval section performs person retrieval processing on each piece of biometric information stored in the memory in descending order of the reliability level corresponding to each piece of biometric information.

Abstract: A face authentication apparatus includes a high tone image acquiring section, a tone converting section, a face characteristic extracting section and a face collation section. The high tone image acquiring section acquires a high tone image containing the face of a walker. The tone converting section converts the acquired high tone image to a low tone image by tone conversion processing which optimizes the brightness of a face area in the high tone image acquired by the high tone image acquiring section. The face characteristic extracting section executes an extraction processing for the face characteristic information based on the low tone image whose brightness is optimized by the tone converting section. Further, the face collation section executes face collation processing based on the low tone image whose brightness is optimized by the tone converting section.

Abstract: A face authentication unit includes an acquisition unit configured to acquire an image including at least the face of a moving person, a face detection unit configured to detect a face region candidate of the person from the image acquired by the acquisition unit, and to detect a face region from the detected face region candidate, a face recognition unit configured to recognize an image of the face region detected by the face detection unit and registered face information which is stored in advance, and an authentication unit configured to authenticate the person based on the recognition result of the face recognition unit.

Abstract: A face image read apparatus which reads the face image of a moving person by the time he or she arrives at a particular position, comprises first and second image capture units configured to capture an area where the moving person enters from first and second directions, respectively, a first detector unit configured to detect the face region of the moving person from an image captured by the first image capture unit, a second detector unit configured to detect whether the number of face regions detected by the first detector unit is one or more, and a switching control unit configured to, according to the result of detection by the second detector unit, switch between a first process based on an image captured by the first image capture unit and a second process based on images captured by the first and second image capture units.

Abstract: In an image compression device, an image input section performs image input processing to input an image having characteristics which are recognition objects, a conversion processing section performs conversion processing to convert a pixel value of each pixel forming the image into a pixel value suitable for coding while retaining the characteristics which are the recognition objects in the image input by the image input processing, and a coding section performs coding processing as compression processing with respect to the image constituted of each pixel converted by the conversion processing.