A system for determining a likelihood that a pre-determined customer will enter a selected customer support center (CSC) is provided. The system includes a customer-tracking system for determining whether the customer is within a predetermined distance of the CSC. The tracking system is set to a tracking state in response to receiving a customer tracking opt-in selection. The system also includes a biometric monitoring system that monitors, using an electronic device in close proximity to the customer, when the customer is determined to be within the pre-determined distance of the CSC. The biometric monitoring system monitors the customer's biometric functions to determine the likelihood of the customer entering the CSC. When a determination of the likelihood of the customer physically entering the CSC is higher than a threshold level, the system may provide the likelihood, as further influenced by the customer sentiment condition, of the customer entering the CSC.

A fingerprint capture system, a fingerprint capture device, an image processing apparatus, a fingerprint capture method, and a storage medium that can acquire a high quality fingerprint image are provided. A disclosed example includes: a capture unit that captures a fingerprint; an image processing unit that processes a transferred fingerprint image captured by the capture unit; a display unit on which the fingerprint image transferred to the image processing unit is displayed; a recording unit where the fingerprint image transferred to the image processing unit is recorded by the image processing unit; and an instruction unit that inputs, in the image processing unit, a record instruction that instructs the image processing unit to record the fingerprint image in the recording unit. The image processing unit records, in the recording unit, the fingerprint image displayed on the display unit at the time the record instruction is input by the instruction unit.

A method and device for behavior recognition are provided. According the present disclosure, an image is collected. It is detected whether the image includes a face. When the image includes a face, it is determined whether the face in the image is a face with closed eyes. When the face in the image is not with closed eyes, a distance between the face and an image collecting unit is computed. A maximum field of vision of a target to which the face belongs is determined according to the distance and a parameter of the image collecting unit. It is determined, according to the maximum field of vision, whether the target has a set behavior.

Techniques are described for improving computer-assisted surgical (CAS) systems, particularly, to recognize surgical phases in a video of a surgical procedure. A CAS system includes cameras that provide video stream of a surgical procedure. According to one or more aspects the surgical phases are automatically detected in the video stream using a machine learning model. Particularly, the machine learning model includes a boundary aware cascade stage network to perform surgical phase recognition.

The present invention achieves a therapy and/or exercise instructing process management that is capable of providing an exercise menu sufficiently suited for an exerciser by considering the state of the body of the exerciser. The above object is achieved to provide a therapy and/or exercise instructing process management system implemented by a client-side terminal operated by a client and a trainer-side terminal that is connectable to the client-side terminal via communication and operated by a trainer, the system including: an assessor that assesses a posture of the client in a steady state and/or a moving state; an exercise determiner that determines exercise to be performed by the client based on an assessment made by the assessor; and a storage that stores information regarding the assessment made by the assessor and information regarding the exercise that is determined by the exercise determiner and performed by the client.

The present invention achieves a therapy and/or exercise instructing process management that is capable of providing an exercise menu sufficiently suited for an exerciser by considering the state of the body of the exerciser. The above object is achieved to provide a therapy and/or exercise instructing process management system implemented by a client-side terminal operated by a client and a trainer-side terminal that is connectable to the client-side terminal via communication and operated by a trainer, the system including: an assessor that assesses a posture of the client in a steady state and/or a moving state; an exercise determiner that determines exercise to be performed by the client based on an assessment made by the assessor; and a storage that stores information regarding the assessment made by the assessor and information regarding the exercise that is determined by the exercise determiner and performed by the client.

An image processing device according to one aspect of the present disclosure includes: at least one memory storing a set of instructions; and at least one processor configured to execute the set of instructions to: receive a visible image of a face; receive a near-infrared image of the face; adjust brightness of the visible image based on a frequency distribution of pixel values of the visible image and a frequency distribution of pixel values of the near-infrared image; specify a relative position at which the visible image is related to the near-infrared image; invert adjusted brightness of the visible image; detect a region of a pupil from a synthetic image obtained by adding up the visible image the brightness of which is inverted and the near-infrared image based on the relative position; and output information on the detected pupil.

An information generation method and apparatus, an electronic device, and a computer readable medium. The method comprises: generating a target region set on the basis of an obtained first target picture (201); selecting a target region satisfying a preset condition from the target region set as a candidate region to obtain a candidate region set (202); generating a second target picture on the basis of the candidate region set (203); and generating picture related information on the basis of the second target picture (204). Thus, effective extraction for a target region and further classification for the target region are achieved, so that an identification result of an image region is finer, and then the subsequent image processing technology has a promotion space.

An apparatus for providing gaze tracking in a near-eye display. Certain examples provide an apparatus including a light modulator configured to receive light of a first range of wavelengths and generate an image beam therefrom. The light modulator is further configured to receive light of a second range of wavelengths and generate a probe beam therefrom. The apparatus also includes one or more light guides including one or more in-coupling element areas, and one or more out-coupling element areas. The one or more in-coupling diffractive element areas are configured to receive and in-couple the image beam and the probe beam into the one or more light guides. The one or more out-coupling element areas are configured to out-couple, from the one or more light guides: the image beam to a user's eye for user viewing, and the probe beam to the user's eye for detection of reflection therefrom.

A measurement unit (110) performs a measurement process for measuring a biological signal (21) from a target person (20). The biological signal (21) contains a plurality of components and can be measured in a manner to be unnoticeable by the target person (20). A component extraction unit (120) extracts an authentication component, which is to be used for authentication, from the plurality of components. A feature amount extraction unit (130) extracts a current feature amount indicating a present feature amount of the authentication component, from the authentication component. A registration unit (140) registers an identifier, which is used for identifying the target person (20), and a template feature amount, which is a feature amount extracted from the target person (20) in the past, in a storage unit (160), as template information (161). A comparison unit (150) compares the current feature amount to the template feature amount. When a difference between the current feature amount and the template feature amount is within a tolerance value (162), the comparison unit (150) returns processing to the measurement process and the authentication is repeated. When the difference between the current feature amount and the template feature amount is larger than the tolerance value (162), the processing is ended.