Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A method of identifying a living being includes using a camera to capture a blurred visual image of an iris of the living being. The blurred visual image is digitally unblurred based on a distribution of eye image gradients in an empirically-collected sample of eye images and characteristics of pupil region. The unblurred image is processed to determine an identity of the living being.

Abstract: Estimating a blur kernel distribution for visual iris recognition includes determining a first mathematical relationship between an in-focus position of a camera lens and a distance between the lens and an iris whose image is to be captured by the lens. A second mathematical relationship between the in-focus position of the lens and a standard deviation defining a Gaussian blur kernel distribution is estimated. The first mathematical relationship is used to ascertain a desired focus position of the lens based upon the actual position of the living being's eye at the point in time. The second mathematical relationship is used to calculate a standard deviation defining a Gaussian blur kernel distribution. The produced image is digitally unblurred by using the blur kernel distribution defined by the calculated standard deviation.

Abstract: A method of identifying a living being includes using a camera to capture a blurred visual image of an iris of the living being. The blurred visual image is digitally unblurred based on a distribution of eye image gradients in an empirically-collected sample of eye images and characteristics of pupil region. The unblurred image is processed to determine an identity of the living being.

Abstract: The described implementations relate to enhancement images, such as in videoconferencing scenarios. One system includes a poriferous display screen having generally opposing front and back surfaces. This system also includes a camera positioned proximate to the back surface to capture an image through the poriferous display screen.

Abstract: A method of estimating a blur kernel distribution for visual iris recognition includes determining a first mathematical relationship between an in-focus position of a camera lens and a distance between the lens and an iris whose image is to be captured by the lens. The first relationship is used to estimate a second mathematical relationship between the in-focus position of the lens and a standard deviation defining a Gaussian blur kernel distribution. A position of an eye of a living being at a future point in time is predicted. A focus position of the camera lens is adjusted based upon the predicted position of the eye. The camera lens with the adjusted focus position is used to produce an image of the living being's eye at the point in time. An actual position of the living being's eye at the point in time is sensed. The first relationship is used to ascertain a desired focus position of the lens based upon the actual position of the living being's eye at the point in time.

Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A method of identifying a living being includes using a camera to capture a blurred visual image of an iris of the living being. The blurred visual image is digitally unblurred based on a distribution of eye image gradients in an empirically-collected sample of eye images and characteristics of pupil region. The unblurred image is processed to determine an identity of the living being.

Abstract: A method of estimating a blur kernel distribution for visual iris recognition includes determining a first mathematical relationship between an in-focus position of a camera lens and a distance between the lens and an iris whose image is to be captured by the lens. The first relationship is used to estimate a second mathematical relationship between the in-focus position of the lens and a standard deviation defining a Gaussian blur kernel distribution. A position of an eye of a living being at a future point in time is predicted. A focus position of the camera lens is adjusted based upon the predicted position of the eye. The camera lens with the adjusted focus position is used to produce an image of the living being's eye at the point in time. An actual position of the living being's eye at the point in time is sensed. The first relationship is used to ascertain a desired focus position of the lens based upon the actual position of the living being's eye at the point in time.

Abstract: A method of identifying a living being includes using a time-of-flight sensor to determine a location of a face of the living being. An image of an iris of the living being is produced dependent upon the location of the face as determined by the time-of-flight sensor. The produced image is processed to determine an identity of the living being.

Abstract: A pixel compositor device for routing an incoming stream of pixel data having been rendered elsewhere and bound for being projected. The device includes a plurality of digital signal inputs each adapted for receiving a plurality of pixel information from the incoming stream of pixel data. The digital signal inputs are in communication with at least one buffer for the pixel information once received by the device. A processing unit is included for image warping the pixel information by performing, on each of a respective of the pixel information: (i) a mapping relating to location of the respective pixel information, and (ii) a scaling function. The geometric mapping can be performed by applying an interpolation technique; and the scaling function can be any of a number of photometric correction functions (alpha-blending, color uniformity correction, image brightness or contrast adjustment, etc.).

Abstract: Video images representative of a conferee's head are received and evaluated with respect to a reference model to monitor a head position of the conferee. A personalized face model of the conferee is captured to track head position of the conferee. In a stereo implementation, first and second video images representative of a first conferee taken from different views are concurrently captured. A head position of the first conferee is tracked from the first and second video images. The tracking of head-position through a personalized model-based approach can be used in a number of applications such as human-computer interaction and eye-gaze correction for video conferencing.

Abstract: Correcting for eye-gaze in video communication devices is accomplished by blending information captured from a stereoscopic view of the conferee and generating a virtual image of the conferee. A personalized face model of the conferee is captured to track head position of the conferee. First and second video images representative of a first conferee taken from different views are concurrently captured. A head position of the first conferee is tracked from the first and second video images. Matching features and contours from the first and second video images are ascertained. The head position as well as the matching features and contours from the first and second video images are synthesized to generate a virtual image video stream of the first conferee that makes the first conferee appear to be making eye contact with a second conferee who is watching the virtual image video stream.

Abstract: Video images representative of a conferee's head are received and evaluated with respect to a reference model to monitor a head position of the conferee. A personalized face model of the conferee is captured to track head position of the conferee. In a stereo implementation, first and second video images representative of a first conferee taken from different views are concurrently captured. A bead position of the first conferee is tracked from the first and second video images. The tracking of head-position through a personalized model-based approach can be used in a number of applications such as human-computer interaction and eye-gaze correction for video conferencing.

Abstract: Correcting for eye-gaze in video communication devices is accomplished by blending information captured from a stereoscopic view of the conferee and generating a virtual image of the conferee. A personalized face model of the conferee is captured to track head position of the conferee. First and second video images representative of a first conferee taken from different views are concurrently captured. A head position of the first conferee is tracked from the first and second video images. Matching features and contours from the first and second video images are ascertained. The head position as well as the matching features and contours from the first and second video images are synthesized to generate a virtual image video stream of the first conferee that makes the first conferee appear to be making eye contact with a second conferee who is watching the virtual image video stream.