Aware, Inc.

Siemens Corporation

Limin Ma

Taras P Riopka

David Benini

Robert Mungovan

Pranab Mohanty

Hong Shen

Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated. Thus, the methodology eliminates false identifications of specific instances of biometric features while accentuating true identifications.

Biometric feature extraction using multiple image instantiations

An optical module includes a housing with a lighting mechanism, an aperture formed therein, and a window that frames a transparent surface adapted to contact a skin surface of a person, for example, a fingertip. The lighting mechanism provides light to illuminate the skin surface placed upon the transparent surface. A prism has a first side facing the lighting mechanism, a second side at the window, and a third side through which a dactylographic image exits the prism. One or more light reflecting surfaces are disposed within the housing to reflect the dactylographic image towards the housing aperture. The optical module is coupled to a mobile device having a camera, with the aperture of the housing aligning with a lens of the camera. The camera acquires the dactylographic image, and the mobile device adjusts this dactylographic image to produce a dactylographic image suitable for biometric matching.

System and method of capturing and producing biometric-matching quality fingerprints and other types of dactylographic images with a mobile device

A method and system for automatically detecting rib metastasis in a thoracic CT volume is disclosed. The ribs are segmented in said CT volume by recursive tracing. A series of cross-sectional images are then generated along a centerline of each rib. Cortical and trabecular bone structures are segmented in each of the cross-sectional images for each rib. Features are calculated for each cross-sectional image based on characteristics of the cortical and trabecular bone structures, and alterations are detected in the cross-sectional images based on the features. Rib metastasis is detected in a rib when an alteration is detected in a number of consecutive cross-sectional images along the centerline of the rib.

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Lowell, MA, United States of America

Limin Ma