Abstract: A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

Abstract: A method of authenticating users is provided that includes storing data in a buffer. The data is within a temporal window and includes biometric data extracted from frames included in a video and quality feature values calculated for each frame. Each quality feature value corresponds to a different quality feature. Moreover, the method includes calculating a score for each different quality feature using the corresponding quality feature values, and determining a most recent frame included in the video includes biometric data usable in a biometric authentication matching transaction when the calculated score for each different quality feature satisfies a respective threshold score value.

Abstract: A method for detecting biometric characteristics in a captured biometric data image is provided that includes determining, by a processor, an approximate location for a biometric characteristic in a frame included in captured biometric data, and determining region of interest positions over the frame. Moreover, the method includes calculating a set of feature values for each position, generating a displacement for each set of feature values and generating a median displacement, and adjusting the biometric characteristic location by the median displacement.

Abstract: The present application describes a SoC device with observer units for monitoring a state of a respective functional unit of the SoC combiner units for generating combinational events based on one or more observer events issued by the observer units and one or more feedback events from issued by action units and action units for generating feedback events and/or action request based the on at least one combinational event.

Abstract: A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

Abstract: A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

Abstract: A method for detecting biometric characteristics in a captured biometric data image is provided that includes determining, by a processor, an approximate location for a biometric characteristic in a frame included in captured biometric data, and determining region of interest positions over the frame. Moreover, the method includes calculating a set of feature values for each position, generating a displacement for each set of feature values and generating a median displacement, and adjusting the biometric characteristic location by the median displacement.

Abstract: One embodiment of the invention provides an image processing method for use in fitting a deformable shape model to an acquired image. The deformable shape model specifies a set of target points whose motion is governed by the model. The method comprises, for each target point, generating a corresponding response image by: providing a feature detector to locate a given target point within the acquired image, wherein said feature detector includes a random forest comprising one or more decision trees; scanning a patch image across the acquired image to define multiple sampling locations with respect to the acquired image; for each sampling location of the patch image with respect to the acquired image, performing regression voting using the random forest to produce one or more votes for the location of the given target point within the acquired image; and accumulating the regression votes for all of the multiple sampling locations to generate said response image corresponding to the given target point.

Abstract: A method for detecting biometric characteristics in a captured biometric data image is provided that includes determining, by a processor, an approximate location for a biometric characteristic in a frame included in captured biometric data, and determining region of interest positions over the frame. Moreover, the method includes calculating a set of feature values for each position, generating a displacement for each set of feature values and generating a median displacement, and adjusting the biometric characteristic location by the median displacement.

Abstract: A method for detecting biometric characteristics in a captured biometric data image is provided that includes determining, by a processor, an approximate location for a biometric characteristic in a frame included in captured biometric data, and determining region of interest positions over the frame. Moreover, the method includes calculating a set of feature values for each position, generating a displacement for each set of feature values and generating a median displacement, and adjusting the biometric characteristic location by the median displacement.

Abstract: A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A face detection and/or detection method includes acquiring a digital color image. An active appearance model (AAM) is applied including an interchannel-decorrelated color space. One or more parameters of the model are matched to the image. Face detection results based on the matching and/or different results incorporating the face detection result are communicated.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A technique for determining a characteristic of a face or certain other object within a scene captured in a digital image including acquiring an image and applying a linear texture model that is constructed based on a training data set and that includes a class of objects including a first subset of model components that exhibit a dependency on directional lighting variations and a second subset of model components which are independent of directional lighting variations. A fit of the model to the face or certain other object is obtained including adjusting one or more individual values of one or more of the model components of the linear texture model. Based on the obtained fit of the model to the face or certain other object in the scene, a characteristic of the face or certain other object is determined.

Abstract: A face detection and/or detection method includes acquiring a digital color image. An active appearance model (AAM) is applied including an interchannel-decorrelated color space. One or more parameters of the model are matched to the image. Face detection results based on the matching and/or different results incorporating the face detection result are communicated.