Abstract: Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Abstract: Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Abstract: In one embodiment, the present disclosure provides a computer implemented method of determining energy expenditure associated with a user's movement. A plurality of video images of a subject are obtained. From the plurality of video images, a first location is determined of a first joint of the subject at a first time. From the plurality of video images, a second location is determined of the first joint of the subject at a second time. The movement of the first joint of the subject between the first and second location is associated with an energy associated with the movement.

Abstract: In one embodiment, the present disclosure provides a computer implemented method of determining energy expenditure associated with a user's movement. A plurality of video images of a subject are obtained. From the plurality of video images, a first location is determined of a first joint of the subject at a first time. From the plurality of video images, a second location is determined of the first joint of the subject at a second time. The movement of the first joint of the subject between the first and second location is associated with an energy associated with the movement.

Abstract: For each of at least one digitally-imaged hand part, where each of the at least one digitally-imaged hand part corresponds to one of a plurality of hand parts, a set of feature parameters representing a geometry of the digitally-imaged hand part is computed. The set(s) of feature parameters for a set of one or more of the digitally-imaged hand parts is/are used to compute distances of the set of digitally-imaged hand parts from each of i) a first eigenspace corresponding to a male class, and ii) a second eigenspace corresponding to a female class. The computed distances are used to classify the gender of a hand as belonging to the male class or the female class.

Abstract: For each of at least one digitally-imaged hand part, where each of the at least one digitally-imaged hand part corresponds to one of a plurality of hand parts, a set of feature parameters representing a geometry of the digitally-imaged hand part is computed. The set(s) of feature parameters for a set of one or more of the digitally-imaged hand parts is/are used to compute distances of the set of digitally-imaged hand parts from each of i) a first eigenspace corresponding to a male class, and ii) a second eigenspace corresponding to a female class. The computed distances are used to classify the gender of a hand as belonging to the male class or the female class.

Abstract: A method of determining a match between a candidate template and a super-template involves using the super-template to identify i) spatial coordinates for a plurality of minutiae that define an object, and ii) a quality associated with each minutiae. For sets of the super-template minutiae having at least two defined minutiae qualities, at least one Delaunay triangle is computed for each set. A match between the candidate template and the super-template is determined by determining a correspondence of Delaunay triangles in the candidate and super-templates, which correspondence results in an alignment of at least some minutiae of the candidate template with at least some minutiae of the super-template. Related methods, articles and apparatus are also disclosed.

Abstract: Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.