A non-transitory computer-readable storage medium storing a determination program that causes at least one computer to execute a process, the process includes acquiring a group of captured images that includes images including a face to which markers are attached; selecting, from a plurality of patterns that indicates a transition of positions of the markers, a first pattern that corresponds to a time-series change in the positions of the markers included in consecutive images among the group of captured images; and determining occurrence intensity of an action based on a determination criterion of the action determined based on the first pattern and the positions of the markers included in a captured image included after the consecutive images among the group of captured images.

A face detecting and tracking method includes: acquiring an image and performing a face detection to determine one or more face boxes of each acquired image; performing a detection error removing process to the image of current frame, and saving one of the one or more face boxes of the processed image of current frame as a first face box; and determining a tracking box of the image of current frame based an the first face box and a tracked face box, and tracking the face in the tracking box of the image of current frame.

Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.

Processing for judging whether a face is included in a frame is performed, in a predetermined interval, on each of frames included in a moving image of a subject, displayed on a monitor, until the judgment becomes positive. If it is judged that a face is included in a frame, the facial position is detected in the frame, and stored. Then, judgment is made as to whether a face is included in the next frame after predetermined time. If the judgment is positive, the facial position is detected. The previously stored facial position is replaced by the newly detected facial position, and the newly detected facial position is stored. These processes are repeated until photographing operation is performed by operating a release unit.

A learning apparatus for creating an emotion expression video according to an embodiment disclosed include first generative adversarial networks (GAN) that receive text for creating an emotion expression video, extract vector information by performing embedding on the input text, and create an image based on the extracted vector information, and second generative adversarial networks that receive an emotion expression image and a frame of comparison video, and create a frame of emotion expression video from the emotion expression image and the frame of comparison video.

A collation system of the present invention includes an imaging means for acquiring a captured image of a pre-passage side area with respect to a gate, a collation means for performing a collation process between a previously registered target and a target in the captured image, and a determination means for determining propriety of passage of the target with respect to the gate, on the basis of a result of the collation process. The collation means initiates the collation process on the basis of a condition, set to each area of the captured image, for the target located in the area.

An image processing apparatus includes at least one processor and at least one memory storing executable instructions which, when executed by the at least one processor, cause the image capturing apparatus to acquire an image of at least one person, detect at least one person in the acquired image, control processing for tracking the at least one detected person, and control processing for detecting an action of the at least one detected person based on a result of tracking the at least one detected person. The result of tracking the at least one detected person includes state information representing a tracking state of the at least one detected person. Whether to use the result of tracking the at least one detected person is based on a type of the action detection processing and the state information.

Facial recognition-based authentication comprises obtaining a first image of a target object, updating projection information associated with a display by a display device, obtaining a second image of the target object, the second image being an image of the target object after the projection information is updated, obtaining an image difference data based at least in part on the first image and the second image, and determining whether the target object is a virtual object based at least in part on the image difference data.

The present disclosure is directed toward intelligently mixing and matching faces and/or people to generate an enhanced image that reduces or minimize artifacts and other defects. For example, the disclosed systems can selectively apply different alignment models to determine a relative alignment between a references image and a target image having an improved instance of the person. Upon aligning the digital images, the disclosed systems can intelligently identify a replacement region based on a boundary that includes the target instance and the reference instance of the person without intersecting other objects or people in the image. Using the size and shape of the replacement region around the target instance and the reference instance, the systems replace the instance of the person in the reference image with the target instance. The alignment of the images and the intelligent selection of the replacement region minimizes inconsistencies and/or artifacts in the final image.

An image processing apparatus detects a subject of a first type and a subject of a second type to an image. When executing tracking processing of a subject based on a detection result of the detection circuit, if a same subject is detected as a subject of the first type and a subject of the second type, the image processing apparatus selects either the detection result regarding the subject of the first type or the detection result regarding the subject of the second type is to be used to perform the tracking processing of the subject.