A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.

A device may receive a classification model generated based on a set of spectroscopic measurements performed by a first spectrometer. The device may store the classification model in a data structure. The device may receive a spectroscopic measurement of an unknown sample from a second spectrometer. The device may obtain the classification model from the data structure. The device may classify the unknown sample into a Kosher or non-Kosher group or a Halal or non-Halal group based on the spectroscopic measurement and the classification model. The device may provide information identifying the unknown sample based on the classifying of the unknown sample.

Various embodiments enable an image recognition system reduce the number image match candidates before running a full-fledged pair-wise match on all image match candidates. In order to accomplish this, each inventory image can be assigned to a group. For example, a title for a book sold by an electronic marketplace could be available in multiple languages, in multiple bindings, and the book could be available in print, audio book, or electronic book. Each one of these variations could be associated with its own similarly looking inventory image, each of which could be returned as a valid match to a query image for the book. Accordingly, the inventory images for these variations could be assigned to a group for the book and, instead of geometrically processing an image for each variation, the image match system can process a single image representing all of the variations.

A method for recognizing the face of a user that uses a device in a facility includes receiving a photo of the user and extracting a face image of the user from the photo. The method includes receiving a full candidate face set of all registered users, a first list of registered users that are within the facility, and a second list of registered users that are using other devices in the facility. The method includes generating a reduced candidate face set from the full candidate face set by excluding at least one of (1) candidate faces corresponding to users that are not included in the first list or (2) candidate faces corresponding to users that are included in the second list, from the full candidate face set. The method also includes determining whether the face image corresponds to a candidate face of the reduced candidate face set.

An artificial intelligence-based action recognition method includes: determining, according to video data comprising an interactive object, node sequence information corresponding to video frames in the video data, the node sequence information of each video frame including position information of nodes in a node sequence, the nodes in the node sequence being nodes of the interactive object that are moved to implement a corresponding interactive action; determining action categories corresponding to the video frames in the video data, including: determining, according to the node sequence information corresponding to N consecutive video frames in the video data, action categories respectively corresponding to the N consecutive video frames; and determining, according to the action categories corresponding to the video frames in the video data, a target interactive action made by the interactive object in the video data.

A character recognition device comprising: a storage means storing a plurality of dictionary databases corresponding to each of a plurality of degradation levels, said databases being generated from images wherein an original character image has been degraded using a plurality of different degradation levels; an image input means that receives input of images; an extraction means that extracts character images from said images; a usage order determination means that determines an usage order for dictionary databases on the basis of degradation degrees for extracted character images; and a character recognition means that uses dictionary databases in accordance with said usage order, recognizes characters included in the extracted character image and, if recognition results for the character recognition fulfil prescribed conditions, outputs the recognition results without performing recognition using the dictionary database next in said usage order. As a result, accurate and fast recognition of degraded character images can be performed.

An image processing apparatus includes a plurality of dictionaries configured to store a feature of an object and information on an imaging direction in a scene for each kind of imaged scene, a detecting unit configured to detect an object with reference to at least one of the plurality of dictionaries in the scene in which the object has been imaged and which is to be learned, an estimating unit configured to estimate the imaging direction the detected object, a selecting unit configured to select one dictionary from the plurality of dictionaries based on the imaging direction estimated by the estimating unit and the information on the imaging direction in each of the plurality of dictionaries, and a learning unit configured to learn the dictionary selected by the selecting unit, based on a detection result produced by the detecting unit.

According to an embodiment, a calculation device includes a registerer, a first calculator, a receiver, and a second calculator. The registerer registers a plurality of patterns. The first calculator calculates a degree of closeness between the registered patterns. The receiver receives an input pattern. The second calculator calculates a first similarity value between the input pattern and a first registered pattern among the plurality of registered patterns, calculates second similarity values between the input pattern and one or more second registered patterns having a neighbor relationship with the first registered pattern among the plurality of registered patterns, and calculates a combined similarity value in which the first similarity value and the one or more second similarity values are combined.

An intelligent scoring method and system for a text objective question, the method comprising: acquiring an answer image of a text objective question (101); segmenting the answer image to obtain one or more segmentation results of an answer string to be identified (102); determining whether any of the segmentation results has the same number of characters as the standard answer (103); if no, the answer is determined to be wrong (106); otherwise, calculating identification confidence of the segmentation result having the same number of words as the standard answer, and/or calculating the identification confidence of respective characters in the segmentation result having the same number of words as the standard answer (104); determining whether the answer is correct according to the calculated identification confidence (105). The method can automatically score text objective questions, thus reducing consumption of human resource, and improving scoring efficiency and accuracy.

An object of the present invention is to provide a fingerprint authentication system, a fingerprint authentication program and a fingerprint authentication method that can achieve high-speed processing.

A fingerprint authentication system includes a plurality of stages (n+1 stages) of indexes, a registration part in which fingerprint data FD is registered as fingerprint data TFD according to the indexes, and a checking device that checks the fingerprint data FD against the registered fingerprint data TFD based on the indexes. The fingerprint data TFD can be classified into groups, the processing speed can be increased even if there is a large number of pieces of fingerprint data TFD registered.