An information processing apparatus stores first and second registered feature data respectively expressing first and second features of registered data, generates first and second subject feature data respectively expressing the first and second features of subject data, calculates a first degree of dissimilarity between the first registered feature data and the first subject feature data using a first computational process that exhibits symmetry so that a computational result does not change when two input values are interchanged, calculates a second degree of dissimilarity between the second registered feature data and the second subject feature data using a second computational process that exhibits antisymmetry so that a computational result changes when the two input values are interchanged, and selects the registered data based on the first and second degrees of dissimilarity.

A method including training predictor machine learning models (MLMs) using a first data set. The trained predictor MLMs are trained to predict classifications of data items in the first data set. The method also includes training confidence MLMs using second classifications, output by the trained predictor MLMs. The method also includes generating an aggregated ranked list of classes based on third classifications output by the trained predictor MLMs and second confidences output by the trained confidence MLMs. The method also includes training an ensemble confidence MLM using the aggregated ranked list of classes to generate a trained ensemble confidence MLM. The trained ensemble confidence MLM is trained to predict a corresponding selected classification for each corresponding data item in a training data set containing second data items similar to the first data items.

A recognition apparatus includes an image acquisition unit configured to acquire an image obtained by photographing an object and a recognition processing unit configured to, when it is not clearly determined whether the object is a human being or an animal as a result of detecting the human being or the an animal in the acquired image using a full-body recognition dictionary of the human being and the animal, increase a certainty that the object is the animal to thereby detect the animal when the animal's head, face, or buttocks are detected in a range different from that of a human being within a detection range using a partial-body recognition dictionary, the partial-body recognition dictionary being for detecting the animal's head, face, or buttocks in the detection range in which the human being or the animal is detected.

A method for testing medical data is provided. Each medical datum includes a plurality of information units and a plurality of separators, and the method includes the following steps: a. matching the medical data against a standard library including a plurality of patterns, a matching expression being:

[\s\S][number/sequence/relation]&[\b|\B] (S101); and b. determining, based on a matching result of the step a, whether the medical datum is qualified (S102). A standardized standard library is first established, a matching result is obtained by matching the medical datum and the standard library for a non-initial boundary, an initial boundary, an information quantity, information sequences, a semantic relationship quantity, a character boundary, and a non-character boundary, and whether the medical datum meets a requirement is further determined according to the matching result.

A method for testing medical data is provided. Each medical datum includes a plurality of information units and a plurality of separators, and the method includes the following steps: a. matching the medical data against a standard library including a plurality of patterns, a matching expression being:

[\s\S][number/sequence/relation]&[\b|\B] (S101); and b. determining, based on a matching result of the step a, whether the medical datum is qualified (S102). A standardized standard library is first established, a matching result is obtained by matching the medical datum and the standard library for a non-initial boundary, an initial boundary, an information quantity, information sequences, a semantic relationship quantity, a character boundary, and a non-character boundary, and whether the medical datum meets a requirement is further determined according to the matching result.

A non-transitory computer readable medium includes instructions that, when executed by at least one processor, cause the at least one processor to perform artificial-intelligence-based technical support operations. The operations may include receiving over at least one network first audio signals including speech data associated with a technical support session and first image signals including image data associated with a product for which support is sought from a mobile communications device, analyzing the first audio signals and the first image signals using artificial intelligence, aggregating the analysis thereof, accessing at least one data structure to identify an image capture instruction, presenting the image capture instruction including a direction to alter and capture second image signals of a structure identified in the first image signals to the mobile communications device, receiving from the mobile communications device second image signals, analyzing the same using artificial intelligence, and determining a technical support resolution status.

Techniques for saving bandwidth in performing facial recognition are provided. An image including a face may be received, over a wireless link, at a first resolution. A facial recognition system may identify a subset of people who may be associated with the face, wherein the facial recognition system cannot definitively associate the face with an individual person in the subset of people, based on the image including the face at the first resolution. A feature of the subset of people that may be used to identify a person within the subset of people may be determined. A request for the portion of the image containing the feature at a second resolution may be sent over the wireless link. The second resolution may be higher than the first.

A sequence identification method and apparatus, an image processing device and a storage medium, which belong to the field of image identification, are provided. The method includes: performing feature extraction on a to-be-identified target image through an image identification model to obtain a first feature map, where the first feature map includes a plurality of first image features; performing time sequence relationship extraction on the first feature map based on a convolutional neural network layer and a fully connected layer in the image identification model, to obtain a second feature map that merges upper and lower information included in the to-be-identified target image, where the second feature map includes a plurality of second image features; and performing character identification on the to-be-identified target image in parallel based on the plurality of first image features and the plurality of second image features to obtain a character sequence.

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can he assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented. using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can he assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented. using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.