A method for processing an image, which is performed by an image processing apparatus, is provided. The method includes acquiring a first image including an object and a second image including an object identical to the object in the first image under the same condition, acquiring three-dimensional direction information of a specific part of the object in the first image, and providing a three-dimensionally processed image by three-dimensionally rotating the object in the second image by an angle that corresponds to the acquired three-dimensional direction information of the specific part of the object in the first image.

A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

A method for training a face recognition model includes: acquiring a plurality of first training images being uncovered face images, and acquiring a plurality of covering object images; generating a plurality of second training images by separately fusing the plurality of covering object images with the uncovered face images; and training the face recognition model by inputting the plurality of first training images and the plurality of second training images into the face recognition model.

Techniques for securing displayed data on computing devices are disclosed. One example technique includes upon determining that the computing device is unlocked, capturing and analyzing an image in a field of view of the camera of the computing device to determine whether the image includes a human face. In response to determining that the image includes a human face, the technique includes determining facial attributes of the human face in the image via facial recognition and whether the human face is that of an authorized user of the computing device. In response to determining that the human face is not one of an authorized user of the computing device, the technique includes converting user data on the computing device from an original language to a new language to output on a display of the computing device, thereby securing the displayed user data even when the computing device is unlocked.

Facial recognition adversarial patch adjustment is performed by applying a patch to a digital image including image data representing a face to obtain a patched image, applying a face detector to the patched image to obtain a detection value representing a likelihood that the patched image includes image data representing facial features, applying a feature extractor to the patched image to obtain a feature vector, applying a similarity determiner to the feature vector and a target feature vector to obtain a similarity value representing a likelihood that the patched image includes image data representing a target face, calculating a loss based on the detection value and the similarity value, and adjusting the patch based on the loss.

The coating quality prediction device includes: a learned model that has learned a relationship between characteristics of a paint, conditions at a time of applying the paint, and a smoothness of a surface of a coating film obtained by applying the paint under the conditions; and a calculation unit that uses the learned model to calculate the smoothness of the surface of the coating film from the characteristics of the paint and the conditions at the time of applying the paint.

A camera in a facility acquires an image of a user performing an action, such as shopping. These images may include potentially identifying information, such as an image that includes the user's face. To safeguard privacy of the user, a modified image is generated by changing one or more characteristics of the acquired image. The modification affects the entire image, rendering the resulting image unsuitable for another person to identify the user. For example, a contrast value for the image may be changed, rendering the user unrecognizable. This modification removes the potentially identifying information before later processing takes place. For example, the modified image in which the user's face is obscured may retain other non-identifying information used to train an artificial neural network to recognize actions such as pick or place of items in the facility.

A display device includes a display configured to output image data, and a controller configured to extract a contour noise removal region from the image data, acquire motion information of the image data, and remove contour noise based on a de-contour gain value corresponding to the motion information.

A spoofing detection apparatus 300 includes a face image obtaining unit 301 that obtains a first image frame that includes the face of a subject person obtained with a light-emitting apparatus 320 turned on and a second image frame that includes the face of the subject person obtained with a light-emitting apparatus 320 turned off, a face information extraction unit 302 that extracts first face information, and extracts second face information, a feature value calculation unit 303 that obtains a luminance value of the face portion in the first image frame, obtains a luminance value of the face portion in the second image frame, and calculates, based on these values, a feature value that reflects a three-dimensional shape, and that is independent of colors of a face surface, and a spoofing determination unit 304 that determines authenticity of subject person based on the feature value.

A vehicular driver monitoring system includes a camera and an electronic control unit. The camera is disposed at an interior rearview mirror assembly of the vehicle so as to view at least the face of a driver of the vehicle. The camera captures image data and the captured image data includes image data representative of the face of the driver. The vehicular driver monitoring system detects changes at the driver's eyes via processing by the image processor of image data captured by the camera, and determines driver attentiveness based at least in part on detected changes at the driver's eyes. The vehicular driver monitoring system functions as a remote Photoplethysmography system, and monitors a heart rate of the driver based at least in part on processing at the electronic control unit of image data captured by the camera, and determines impairment of the driver's capability to operate the vehicle.