A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.

The present disclosure relates to an image display apparatus and an operating method thereof. The image display device according to an embodiment of the present disclosure includes a display; a network interface unit that performs communication through a network; and a controller, wherein the controller generates data for a screen output through the display, when a preset user input is received while a first content is output through the display, obtains data, which corresponds to data of the screen, that is related to an object included in the screen, from a first server through the network interface unit, determines at least one first object related to a position corresponding to the user input, among object included in the screen, based on the data that is related to the object, and outputs a user interface (UI) for the at least one first object through the display. Various other embodiments are possible.

Provided are a damage determination information system, a server device, a terminal apparatus, and a program that can realize at least one of the improvement of the convincing feeling for a damage determination result, the reduction of an investigation cost, or the decrease of fluctuation of the damage determination results by a plurality of investigators. A provisional damage determination result is acquired, and a server device extracts a plurality of disaster images related to the provisional damage determination result from a disaster image database (15) and displays the extracted disaster image on a display device of a terminal apparatus. Input of an instruction for settling the damage determination result is received from the terminal apparatus that displays the disaster image, and the settled damage determination result is registered in a damage investigation result database (17).

Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.

In an information processing device (a server device), a first acquirer acquires multiple captured images of the outside of a vehicle. A detector detects, from acquired multiple captured images, multiple images related to unsafe driving using a learned model. An image extractor extracts, from detected multiple images related to unsafe driving, an image to be a candidate for relearning data of the learned model.

Example embodiments that analyze images to characterize aspects of the images rely on a same neural network to characterize multiple aspects in parallel. Because additional neural networks are not required for additional aspects, such an approach scales with increased aspects.

Embodiments are directed to techniques for image content extraction. Some embodiments include extracting contextually structured data from document images, such as by automatically identifying document layout, document data, document metadata, and/or correlations therebetween in a document image, for instance. Some embodiments utilize breakpoints to enable the system to match different documents with internal variations to a common template. Several embodiments include extracting contextually structured data from table images, such as gridded and non-gridded tables. Many embodiments are directed to generating and utilizing a document template database for automatically extracting document image contents into a contextually structured format. Several embodiments are directed to automatically identifying and associating document metadata with corresponding document data in a document image to generate a machine-facilitated annotation of the document image. In some embodiments, the machine-facilitated annotation may be used to generate a template for the template database.

State of art techniques attempt in extracting insights from eye features, specifically pupil with focus on behavioral analysis than on confidence level detection. Embodiments of the present disclosure provide a method and system for confidence level detection from eye features using ML based approach. The method enables generating overall confidence level label based on the subject's performance during an interaction, wherein the interaction that is analyzed is captured as a video sequence focusing on face of the subject. For each frame facial features comprising an Eye-Aspect ratio, a mouth movement, Horizontal displacements, Vertical displacements, Horizontal Squeezes and Vertical Peaks, are computed, wherein HDs, VDs, HSs and VPs are features that are derived from points on eyebrow with reference to nose tip of the detected face. This is repeated for all frames in the window. A Bi-LSTM model is trained using the facial features to derive confidence level of the subject.

An image information overlay system retrieves an image associated with a vehicle listing and uses machine learning models to classify the image, generating identification data that may comprise a vehicle make and model, a feature or part of the vehicle present in the image, and a location of the vehicle feature or part. The identification data or an individual identifier of the vehicle, such as a Vehicle Identification Number (VIN), may be used to retrieve overlay information related to the vehicle make and model, such as recalls or known maintenance issues or information specific to the vehicle, such as mileage, accident reports, or ownership history. The overlay information is displayed on the image as an overlay at the location of the vehicle feature or part corresponding to the overlay information.

A server apparatus includes a controller configured to detect an item to be purchased, based on a temporal change in captured images of an item display position at a mobile shop and, upon acquiring authentication information for a purchaser from a first terminal apparatus, perform a charging process in respect of the purchaser for a price of the item.