[Object] To provide an information processing apparatus, an information processing method, and a program, which can improve accuracy of authentication based on biometric information of an eye. [Solution] Provided is an information processing apparatus including: a biometric information authentication unit that authenticates biometric information identified from each of a plurality of captured images of an eye of a user of different sight line directions, on the basis of reference images of the eye of the user of the respective sight line directions; and an authentication result combining unit that combines authentication results by the biometric information authentication unit.

Embodiments of the present disclosure provide a fingerprint identification module, a fingerprint identification method and a display device. The fingerprint identification module includes: a capacitive sensor having a plurality of capacitive electrodes which do not contact with each other, the capacitive sensor being configured to identify a fingerprint of a finger based on induced capacitances generated between the capacitive electrodes and ridges in an epidermal layer of the finger and generated between the capacitive electrodes and valleys in the epidermal layer; a radio-frequency sensor having a plurality of radio-frequency receiving electrodes which do not contact with each other, the radio-frequency sensor being configured to identify the fingerprint based on reflected signals received by the radio-frequency receiving electrodes; and a controller configured to activate the radio-frequency sensor to identify the fingerprint when fingerprint information identified by the capacitive sensor is not consistent with fingerprint information pre-stored in the fingerprint identification module.

An adaptive action recognizer for video that performs multiscale spatiotemporal decomposition of video to generate lower complexity video. The adaptive action recognizer has a number of processing pathways, one for each level of video complexity with each processing pathway having a different computational cost. The adaptive action recognizer applies a decision making scheme that encourages using low average computational costs while retaining high accuracy.

An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

An apparatus and method for recognizing a driving lane of a vehicle are provided. The apparatus includes an information acquisition device that acquires forward information of a road on which the vehicle is driving, and a processor that recognizes an entrance section and an exit section on a front left side of the vehicle based on the forward information, and determines a driving lane of the vehicle by correcting a number of lanes of the road in the entrance section and the exit section.

This invention is related to a method to improve the performance of a deep neural network (10) for the identification of a segmentation target (111) in a medical image (12, 110), comprising the steps of performing n training steps on said deep neural network (10) for the identification of said region of interest on two different representations (13, 14) of the same segmentation target (111), said representations (13,14) being definitions of the same segmentation target (111).

Some embodiments of the invention provide a method for identifying geographic locations and for performing a fine-grained classification of elements detected in images captured from multiple different viewpoints or perspectives. In several embodiments, the method identifies the geographic locations by probabilistically combining predictions from the different viewpoints by warping their outputs to a common geographic coordinate frame. The method of certain embodiments performs the fine-grained classification based on image portions from several images associated with a particular geographic location, where the images are captured from different perspectives and/or zoom levels.

Systems and methods for training a recursive neural network (RNN) is provided. The method includes generating, by the processor using the RNN, a plurality of embedding vectors based on a plurality of observations, wherein the observations include (i) a subject, (ii) an action taken by the subject, and (iii) an object on which the subject is taking the action on, wherein the subject and object are constant. The method further includes generating, by the processor, predictions of one or more future events based on one or more comparisons of at least some of the plurality of embedding vectors. The method also includes initiating, by the processor, based on the predictions, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.

A computer-implemented method of recognizing a facial expression of a subject in an input image is provided. The method includes filtering the input image to generate a plurality of filter response images; inputting the input image into a first neural network; processing the input image using the first neural network to generate a first prediction value; inputting the plurality of filter response images into a second neural network; processing the plurality of filter response images using the second neural network to generate a second prediction value; weighted averaging the first prediction value and the second prediction value to generate a weighted average prediction value; and generating an image classification result based on the weighted average prediction value.

The present disclosure relates, among other things, to an image analysis method for identifying objects belonging to a particular objet class in a digital image of a biological sample. The method may include, among other things, analyzing the digital image for automatically or semi-automatically identifying objects in the digital image; analyzing the digital image for identifying, for each object, a first object feature value of a first object feature of said object; analyzing the digital image for computing one or more first context feature values; inputting both the first object feature value of each of the objects in the digital image and the first context feature value of said digital image into a first classifier; and executing the first classifier.