Computer systems and methods are provided for receiving a first authentication request that includes an image of an identification document. A risk value is determined using one or more information factors that correspond to the authentication request. A validation user interface that includes the image of the identification document is displayed. A risk category that corresponds to the risk value is determined using at least a first risk threshold. In accordance with a determination that the risk value corresponds to a first risk category, a visual indication that corresponds to the first risk category is displayed. In accordance with a determination that the risk value corresponds to a second risk category, a visual indication that corresponds to the second risk category is displayed.

A facial verification method includes separating a query face image into color channel images of different color channels, obtaining a multi-color channel target face image with a reduced shading of the query face image based on a smoothed image and a gradient image of each of the color channel images, extracting a face feature from the multi-color channel target face image, and determining whether face verification is successful based on the extracted face feature.

Apparatuses, methods, systems, and program products are disclosed for dynamic field of view selection in video. An apparatus includes a processor and memory that stores code executable by the processor to capture a 360-degree video using a 360-degree camera system, detect a direction that a user is looking within the 360-degree video captured using the 360-degree camera system, and set a field of view for the 360-degree video based on the detected direction that the user is looking.

A method includes generating, based on user images, a user 3-D model. The method proceeds with obtaining, via a user interface, a request to graphically represent an accessory on to a user graphical representation. This user graphical representation is generated using the user 3-D model. In response to this request, an accessory 3-D model is obtained. Further, the method includes positioning, via the user interface and based on parameters of the user 3-D model and of the accessory 3-D model, an accessory graphical representation on to the user graphical representation. The method further includes updating, in response to detecting user movement, the user 3-D model and the accessory 3-D model and presenting, via the user interface and based on these updated 3-D models, the accessory graphical representation and the user graphical representation in accordance with the user movement.

The present invention relates to a deep learning-based multiple skin lesion detection system, a multiple lesion detection method, and a computer-readable recording medium that has a program for implementing same recorded thereon. The system according to the present invention enables accurate classification and detection of various skin lesions having similar characteristics, on the basis of a context-dependent decision-making structure in which the local spatial correlation between various skin lesions in skin is considered.

A system for data recording across a network includes a session border controller connecting incoming data from the network to an endpoint recorder. A load balancer is connected to the network between the session border controller and the endpoint and receives the incoming data from the session border controller, wherein the load balancer comprises computer memory and a processor configured to parse the incoming data into video data and audio data according to identification protocols accessible by the processor from the computer memory. A recording apparatus includes recording memory that receives the incoming data from the load balancer, stores a duplicate version of the incoming data in the recording memory, and connects the incoming data to the endpoint.

An image processing system may include an imaging device for capturing an image and an image processing apparatus for processing the image. The imaging device may include an imaging unit for capturing the image, a first recording unit for recording information relating to the image, the information being associated with the image, and a first transmission control unit for controlling transmission of the image to the image processing apparatus. The image processing apparatus may include a reception control unit for controlling reception of the image transmitted from the imaging device, a feature extracting unit for extracting a feature of the received image, a second recording unit for recording the feature, extracted from the image, the feature being associated with the image, and a second transmission control unit for controlling transmission of the feature to the imaging device.

A method of generating one or more new digital images using an original digitally-acquired image including a selected image feature includes identifying within a digital image acquisition device one or more groups of pixels that correspond to the selected image feature based on information from one or more preview images. A portion of the original image is selected that includes the one or more groups of pixels. The technique includes automatically generating values of pixels of one or more new images based on the selected portion in a manner which includes the selected image feature within the one or more new images.

A driver login device includes: a network communication unit in communication with the server via a network; an image pickup device capturing a target to obtain a login data; an image processing unit electrically coupled to the image pickup unit, and receiving and processing the login data to obtain a digital data; and a central processing unit electrically coupled to the network communication unit, the image pickup device and the image processing unit. The central processing unit, by way of the network communication unit, receives the data of the driver from the server via the network, controls the image pickup device to capture the target, comparing the digital data with the data of the driver to generate a comparing result, and by way of the network communication unit, transmitting the comparing result back to the server via the network.

The accuracy of an image matching process can be improved by determining relevant swatch regions of the images, where those regions contain representative patterns of the items of interest represented in those images. Various processes examine a set of visual cues to determine at least one candidate object region, and then collate these regions to determine one or more representative swatch images. For apparel items, this can include locating regions such as an upper body region, torso region, clothing region, foreground region, and the like. Processes such as regression analysis or probability mapping can be used on the collated region data (along with confidence and/or probability values) to determine the appropriate swatch regions.