Abstract: A method comprising using at least one hardware processor to: load a touchless finger/face application onto user system; receive enrollment information through the touchless finger/face application; prompt a user to capture four-finger fingerprint image from a right and/or left; capture the finger prints using a camera included with the user system; simultaneously deploy face detection to sense presence of a user face; once a face is detected, capture face images as a second biometric modality; render the fingerprints into high contrast finger images; and generate a biometric record containing the rendered fingerprints face images.

Abstract: A method comprising using at least one hardware processor to: control a camera to begin at a prescribed starting position and move incrementally to capture a series of images of at least one fingerprint; once the images are captured, evaluating the captured images for best focus using an algorithm designed expressly for fingerprint ridge structure, wherein the focus in each frame can be determined by taking the average per pixel convolution value of a Laplace filter over a small region of the full resolution image and wherein the Laplace filter comprises: capturing an image at an initial focus distance, convolving the captured image with Laplacian of Gaussian kernel, assigning a score to the filtered image reflecting the amount to fine edge resolution, and dynamically updating the focus until an optimal distance is found.

Abstract: A method comprising using at least one hardware processor to: obtain using a mobile device and in association with a transaction, fingerprints and other biometric markers of a user; as the biometric features are captured, the mobile device employing liveness detection and coordination of motion within images of face and hands; storing the biometric features in a secure escrow; and when a fraudulent incident is associated with the user, retrieving the biometric data and transmitting the biometric data to an appropriate authority as “latent prints”.

Abstract: In an automated method of processing fingerprint images, identity information is extracted from prints typically classified as having “no identification value” because of sparse or missing minutiae by capturing ridge information. Bezier approximations of ridge curvature are used as Ridge Specific Markers. Control points arising from Bezier curves generate unique polygons that represent the actual curve in the fingerprint. These polygons are generated for latent prints and reference prints and compared to determine whether a match exists.

Abstract: A mobile device comprising memory configured to store instructions; and a processor, the instructions configured to cause the processor to: capture small specimens of livescan fingerprint ridges using a scaled down fingerprint sensor, use RSM matching to find the ridge structure in a matching reference, use Level 3 Features to confirm the livescan-to-reference match, and use Level 3 features selectively based on “guidance” provided using Level 2 features.

Abstract: A system for transforming an image of a fingerprint, comprises a mobile device, comprising: a first communication interface, a camera configured to capture at least one image of at least one fingerprint, and a mobile device processor configured to execute instructions, the instructions configured to cause the mobile device processor to receive the image from the camera and transmit them to an image processing system via the first communication interface; and an image processing system, comprising: a second communication interface configured to receive the image, and an image processor configured to execute instructions, the instructions configured to cause the image processor receive the image form the second communication interface, and: render the image into a high contrast image, establish focus and image resolution for the image, perform noise reduction on the image, and perform distortion elimination on the image.

Abstract: A system for transforming an image of a fingerprint, comprises a mobile device, comprising: a first communication interface, a camera configured to capture at least one image of at least one fingerprint, and a mobile device processor configured to execute instructions, the instructions configured to cause the mobile device processor to receive the image from the camera and transmit them to an image processing system via the first communication interface; and an image processing system, comprising: a second communication interface configured to receive the image, and an image processor configured to execute instructions, the instructions configured to cause the image processor receive the image form the second communication interface, and: render the image into a high contrast image, establish focus and image resolution for the image, perform noise reduction on the image, and perform distortion elimination on the image.

Abstract: Identity caddy for real-time determination of identity in the mobile environment. The identity caddy offers a means for expanding protection beyond what a password can provide by establishing actual identity through biometric features and behavior characteristics. Furthermore, identity is established from multiple sources (including biometrics as well as locational and environmental factors), and once the identify caddy obtains this information, it corroborates identity by considering them all in tandem through fusion. In an aspect, identity is built from several measures, including biometric features as well as non-biometric features, such as location and travel patterns. Melding identity with portable devices opens the door to numerous new applications for transaction and access controls rooted in identity and supporting commerce, as well as many forms of physical and cyber-security.

Abstract: A method for creating a modeling structure for classifying objects in an image comprises converting an image into digital image data; using a processor, simplifying the digital image data; using the processor, isolating objects in the simplified digital image data; using the processor, creating graphs of the isolated objects, the graphs comprising vertices and edges; using the processor, converting the graphs into representative graph data structures, the graph data structures comprising a database key based on the vertices and edges.

Abstract: In an automated method of processing fingerprint images, identity information is extracted from prints typically classified as having “no identification value” because of sparse or missing minutiae by capturing ridge information. Bezier approximations of ridge curvature are used as Ridge Specific Markers. Control points arising from Bezier curves generate unique polygons that represent the actual curve in the fingerprint. These polygons are generated for latent prints and reference prints and compared to determine whether a match exists.

Abstract: A method for generating architectural design requirements for a building structure is disclosed. A schematic drawing file of the building structure is loaded. The building type for the building structure is designated. A topology mask identifying the functional zones demarcated by the schematic drawing is created over the schematic drawing. Architectural design standards that are specific to the building type designated are applied to each of the identified functional zones to generate the architectural design requirements for the building structure. The architectural design standards being stored in a relational database.

Abstract: A method for recognizing a character string on a static document is disclosed. The character string is extracted from the static document. The character string is converted into a representative character string graph. The common embedded isomorphic graphs are extracted from the representative character string graph. Each of the common embedded isomorphic graphs extracted are converted into digital ink files. The character string associated with each of the digital ink files are identified using a dynamic recognition system.

Abstract: A method for fingerprint recognition comprises converting fingerprint specimens into electronic images; converting the electronic images into mathematical graphs that include a vertex and an edge; detecting similarities between a plurality of graphs; aligning vertices and edges of similar graphs; and comparing similar graphs.

Abstract: A data capture and mining method which enables identification of characters and words through their visible patterns. Specifically, a data capture and mining method involves searching a scanned image using isomorphic, graphical pattern matching techniques that eliminate both the need to convert imaged writing to electronic format through, e.g., OCR and the subsequent need to convert the electronic text into English.

Abstract: A biometric handwriting identification system converts characters and a writing sample into mathematical graphs. The graphs comprise enough information to capture the features of handwriting that are unique to each individual. Optical character recognition (OCR) techniques can then be used to identify these features in the handwriting sample so that drafts from two different samples can be aligned to compare to determine if the features in the writing sample correlate with each other.

Abstract: A method for creating a modeling structure for classifying objects in an image comprises converting an image into digital image data; using a processor, simplifying the digital image data; using the processor, isolating objects in the simplified digital image data; using the processor, creating graphs of the isolated objects, the graphs comprising vertices and edges; using the processor, converting the graphs into representative graph data structures, the graph data structures comprising a database key based on the vertices and edges.

Abstract: A biometric handwriting identification system converts characters and a writing sample into mathematical graphs. The graphs comprise enough information to capture the features of handwriting that are unique to each individual. Optical character recognition (OCR) techniques can then be used to identify these features in the handwriting sample so that drafts from two different samples can be aligned to compare to determine if the features in the writing sample correlate with each other.

Abstract: A data capture and mining method which enables identification of characters and words through their visible patterns. Specifically, a data capture and mining method involves searching a scanned image using isomorphic, graphical pattern matching techniques that eliminate both the need to convert imaged writing to electronic format through, e.g., OCR and the subsequent need to convert the electronic text into English.

Abstract: An image recognition system includes a method and apparatus in which images are characterized and compared on the basis of internal structure, which is independent of image size and image orientation. A library of reference images is first generated and stored, then each input image, or test image, is compared to the images stored in the library until a match is found. The image is represented in memory as nodes, lines, and curves. A plurality of descriptors, called reference keys and reference series, are generated for both the reference images and the test image. The reference library is screened for likely matches by comparing the descriptors for the test image to the descriptors in the reference images in the library. Inclusionary and exclusionary tests are performed. After screening, the each of candidate reference images is searched by comparing the pathway thorough the reference image and the pathway through the test image, and by the degree of correlation between the reference and test images.