An inspection apparatus includes an image sensor including a first imaging region and a reference imaging region. The first imaging region includes first pixels which capture an image of an object in a first wavelength band and output first feature quantities each of which corresponds to one of first pixels. The reference imaging region includes reference pixels which capture an image of the object in a reference wavelength band, which overlaps with the first wavelength band, and output reference feature quantities each of which corresponds to one of reference pixels. An image processing device obtains a reflectance of the object in the first wavelength band based on at least one first feature quantity and reference feature quantity and determines physical properties of the object based on the reflectance. The image processing device corrects the image of the object generated according to output from the image sensor based on the at least one first feature quantity and reference feature quantity.

A system and method for detecting seatbelt positioning includes capturing, by a camera, a near infrared (NIR) image of an occupant, applying a median filter to the NIR image to remove glints; converting the NIR image to a black-and-white image, scanning across the black-and-white (B/W) image to detect a plurality of transitions between black and white segments corresponding to stripes extending lengthwise along a length of the seatbelt, and using detections of the plurality of transitions to indicate a detection of the seatbelt. Converting the NIR image to the black-and-white image may include using a localized binary threshold to determine whether a given pixel in the B/W image should be black or white based on whether a corresponding source pixel within the NIR image is brighter than an average of nearby pixels within a predetermined distance of the corresponding source pixel.

Disclosed herein are methods and system for determining whether a user is wearing a mask, comprising receiving one or more infrared images depicting the user's face in one or more infrared spectral ranges and one or more visible light images depicting the user's face in visible light spectral range, registering the infrared image(s) to the visible light image(s), computing luminance values of a plurality of pixels relating to the user's face in the visible light image(s), computing infrared reflectiveness values of corresponding pixels in the registered infrared light image(s), computing, for each of the pixels, a difference between the luminance value and the infrared reflectiveness value and determining the user is genuine and not wearing a mask in case an aggregated difference aggregating the difference values of the pixels relating to the user's face exceeds a certain value.

A general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources is provided. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds. Having performed a series of data collections, we run a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. The invention follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.

A general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources is provided. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds. Having performed a series of data collections, we run a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. The invention follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.

An imaging device having an imager that includes first pixels having sensitivity to a first light and second pixels having sensitivity to a second light, a wavelength of the first light being different from a wavelength of the second light. The imager being configured to acquire first image data from the first pixels and being configured to acquire second image data from the second pixels. Each of the first image data and the second image data including an image of a code, the code being configured to output the second light. The imaging device further including an image processor configured to extract an image of the code based on the first image data and the second image data.

An imaging device having an imager that includes first pixels having sensitivity to a first light and second pixels having sensitivity to a second light, a wavelength of the first light being different from a wavelength of the second light. The imager being configured to acquire first image data from the first pixels and being configured to acquire second image data from the second pixels. Each of the first image data and the second image data including an image of a code, the code being configured to output the second light. The imaging device further including an image processor configured to extract an image of the code based on the first image data and the second image data.

According to the techniques of this disclosure, a method includes capturing, using a camera system of a vehicle, at least one image of an occupant of the vehicle, determining, based on the at least one image of the occupant, a location of one or more eyes of the occupant within the vehicle, and determining, based on the at least one image of the occupant, an eye gaze vector. The method may also include determining, based on the eye gaze vector, the location of the one or more eyes of the occupant, and a vehicle data file of the vehicle, a region of interest from a plurality of regions of interests of the vehicle at which the occupant is looking, wherein the vehicle data file specifies respective locations of each of the plurality of regions of interest, and selectively performing, based on the region of interest, an action.

A method and system for marking and encoding recyclability includes material identification markers to enable a robot with computer vision capability to properly identify and distinguish between various materials. The material identification markers could include marks or symbols to describe a recyclable item or material. The material identification markers may be incorporated into the design of the item, printed on the item or label, or placed in any other way as this is a vision-based system. Further, the material identification markers may be visible or invisible to the human eye.

When a software update is provided to a device that implements a facial recognition authentication process, a new authentication algorithm to operate the facial recognition authentication process may be included as part of software update. For a period of time, the new authentication algorithm may operate a “virtual” facial recognition authentication process alongside operation of the existing facial recognition authentication process using the existing (e.g., earlier version) authentication algorithm. The performance of the new authentication algorithm in providing facial recognition authentication (as assessed by the “virtual” process) may be compared to the performance of the existing authentication algorithm in providing facial recognition authentication during the period of time. When the performance of the new authentication algorithm is determined to have a satisfactory performance, operation of the actual facial recognition authentication process on the device may be switched to the new authentication algorithm.