The technology described in this document can be embodied in a method for generating an iris identifier. The method includes obtaining a plurality of images of an iris, and generating a binary code for each of the plurality of images of the iris, the binary code including a sequence of bits. The method also includes identifying a first pattern of bits for which bit values and bit-locations are the same across a plurality of the binary codes, generating a first index based on the first pattern of bits, and then storing the first index on a storage device in accordance with a database management system. The first index is linked to biometric information of a different modality for a corresponding user.

This disclosure is directed to calibrating sensor arrays, including sensors arrays mounted on an autonomous vehicle. Image data from multiple cameras in the sensor array can be projected into other camera spaces using one or more dense depth maps. The dense depth map(s) can be generated from point cloud data generated by one of the sensors in the array. Differences determined by the comparison can indicate alignment errors between the cameras. Calibration data associated with the errors can be determined and used to calibrate the sensor array without the need for calibration infrastructure.

An estimation apparatus includes an acquisition unit configured to acquire data about a domesticated animal identified by identification information that is transmitted by a client terminal, and an estimation unit configured to perform estimation by inputting the acquired data about a domesticated animal to a trained model generated by performing machine learning based on captured image data of domesticated animals and collected data about domesticated animals and provide, to the client terminal, an estimation result indicating a result of the estimation, and the client terminal includes a presenting unit configured to transmit a request for estimation together with identification information for identifying a domesticated animal targeted for estimation, receive the estimation result, and present body weight data about the domesticated animal targeted for estimation to a user.

The present disclosure provides a face identification method and a terminal device using the same. The method includes: obtaining a to-be-detected image; performing a brightness enhancement process on the to-be-detected image based on a preset second calculation method to generate a to-be-identified face image; obtaining a first channel value of each channel corresponding to each pixel in the to-be-identified face image; performing another brightness enhancement process on the to-be-identified face image based on each first channel value and a preset first calculation method to obtain a target to-be-identified face image; and performing a face identification process on the target to-be-identified face image to obtain an identification result. Through the above-mentioned scheme, an enhanced face identification manner for the images of low brightness is provided.

A system and method for identifying media segments using audio augmented image cross-comparison is disclosed, in which a media segment identifying system analyses both audio and video content, producing a unique identifier to compare with previously identified media segments in a media segment database. The characteristic landmark-linked-image-comparisons are constructed by first identifying an audio landmark. The audio landmark is an audio peak that exceeds a predetermined threshold. Two digital images are then obtained, one associated directly with the audio landmark, and one obtained a predetermined landmark time removed from the first image. The two images are then used to provide a characteristic landmark-linked-image-comparison. The pair of images are reduced in pixel size and converted to gray scale. Corresponding pixels are compared to form a numeric comparison. One image is mirrored before comparison to reduce the possibility of null comparisons.

A system, method and program product for implementing image recognition. A system is disclosed that includes a training system for generating a multi-feature multi-matcher fusion (MMF) predictor for scoring pairs of images, the training system having: a neural network configurable to extract a set of feature spaces at different resolutions based on a training dataset; and an optimizer that processes the training dataset, extracted feature spaces and a set of matcher functions to generate the MMF predictor having a series of weighted feature/matcher components; and a prediction system that utilizes the MMF predictor to generate a prediction score indicative of a match for a pair of images.

The present disclosure is directed to a system for generating a personality assessment that uses multimodal behavioral signal processing technology and machine learning prediction technology. This system takes a video as input, processes it through an artificial intelligence software built for extracting hundreds of behavioral features, and consequently generates an accurate and reliable personality assessment with its machine-learning predictive software. The personality assessment is based on the five-factor model (FFM), also known as the big 5 personality traits.

Methods and systems for generating a list of products each matching a reference product are disclosed. A user query is first received, and multi-modal attribute data for the reference product are determined, with each data mode being a type of product characterization having a modality selected from a text data class, categorical data, a pre-compared engineered feature, audio, image, and video. Next, a first list of candidate products is determined based on a product match signature, and a second list of candidate products is generated from the first, wherein for at least one given candidate product, a deep learning multi-modal matching model is selected to determine whether a match is found. Lastly, the second list is filtered to remove outliers and to generate the list of matching products. Also disclosed are benefits of the new methods and systems, and alternative embodiments of the implementation.

Described herein are systems, methods, and non-transitory computer readable media for constructing and utilizing vehicle field-of-view (FOV) information. The FOV information can be utilized in connection with vehicle localization such as localization of an autonomous vehicle (AV), sensor data fusion, or the like. A customized computing machine can be provided that is configured to construct and utilize the FOV information. The customized computing machine can utilize the FOV information, and more specifically, FOV semantics data included therein to manage various data and execution patterns relating to processing performed in connection with operation of an AV such as, for example, data prefetch operations, reordering of sensor data input streams, and allocation of data processing among multiple processing cores.

The present disclosure is directed to a system for generating a personality assessment that uses multimodal behavioral signal processing technology and machine learning prediction technology. This system takes a video as input, processes it through an artificial intelligence software built for extracting hundreds of behavioral features, and consequently generates an accurate and reliable personality assessment with its machine-learning predictive software. The personality assessment is based on the five-factor model (FFM), also known as the big 5 personality traits.