A method is for processing a plurality of finger biometric enrollment data sets that accounts for uncertainty in alignment between finger biometric enrollment data sets. The method may include generating a respective estimated physical transformation between each pair of finger biometric enrollment data sets, and generating a respective uncertainty for each estimated physical transformation. The method may also include associating the respective estimated physical transformation and the uncertainty for each estimated physical transformation with each pair of finger biometric enrollment data sets to define a logical finger biometric enrollment graph. The logical finger biometric enrollment graph is readily used for subsequent accurate matching in either touch sensor applications or slide sensor applications.
Spot-Based Finger Biometric Processing Method And Associated Sensor
A method for finger biometric processing may include selecting at least one enrollment spot from finger biometric enrollment data comprising a plurality of pixels, and selecting at least one verification spot from finger biometric verification data comprising a plurality of pixels. One or more spot properties is determined for one (or both) of the at least one enrollment spot and the at least one verification spot. The method may further include comparing the at least one enrollment spot with the at least one verification spot based upon a function of corresponding pixel values of the at least one enrollment spot and the at least one verification spot, and also based upon the at least one spot property.
Finger Sensing Apparatus Using Encrypted User Template And Associated Methods
Michael Boshra - Indialantic FL, US Robert Scott Brandt - Satellite Beach FL, US Jeffrey C. Lee - Melbourne FL, US Gregory Thomas Minteer - Indian Harbour Beach FL, US Gary S. Porter - Malabar FL, US Andrew J. Vandamia - Rockledge FL, US James R. Waldron - Oviedo FL, US
Assignee:
Authentec, Inc. - Melbourne FL
International Classification:
G06F 21/00 G06K 9/00 G05B 23/00
US Classification:
713186, 382115, 382128, 340 58
Abstract:
A finger sensing apparatus may include an integrated circuit (IC) substrate, an array of finger sensing elements on the IC substrate, and encryption circuitry on the IC substrate cooperating with the array of finger sensing elements for encrypting a user template comprising finger template data and at least one user credential. The at least one user credential may enable another device, such as a host platform, to perform at least one protected operation.
Software Based Method For Finger Spoof Detection And Related Devices
A finger sensor may include a finger sensing area and a controller cooperating with the finger sensing area for storing enrollment data including finger feature locations. The controller may be for generating authentication data including finger feature locations based upon positioning of an object adjacent the finger sensing area. The controller may also be for performing aligning the authentication data and the enrollment data, matching between the aligned enrollment and authentication data, and spoof attempt detecting based upon corresponding pairs of finger features and their spatial locations in the aligned enrollment and authentication data. The controller may further be for performing an authentication decision based upon the matching and spoof detecting.
Methods For Matching Ridge Orientation Characteristic Maps And Associated Finger Biometric Sensor
A method for comparing a finger biometric verify ridge orientation characteristic map to a finger biometric enrollment ridge orientation characteristic map may include generating a first probability distribution function substantially for corresponding values of the verify and enrollment ridge orientation characteristic maps that differ from one another by less than or equal to a threshold difference. A second probability distribution function may be generated substantially for corresponding values of the verify and enrollment ridge orientation characteristic maps that differ from one another by more than the threshold difference. The verify ridge orientation characteristic map may be compared to the enrollment ridge orientation characteristic map to determine a match therewith based upon the first and second probability distribution functions.
Finger Sensing Device Using Indexing And Associated Methods
Kuntal Sengupta - Belmont MA, US Michael Boshra - Indialantic FL, US
Assignee:
AuthenTec, Inc. - Melbourne FL
International Classification:
G06K 9/00
US Classification:
382124
Abstract:
A finger sensing device includes a finger sensing area, and a processor cooperating therewith for reducing a number of possible match combinations between a sensed finger data set and each of a plurality of enrolled finger data sets. The processor may reduce the number of possible match combinations by generating a plurality of overlap hypotheses for each possible match combination, generating a co-occurrence matrix score based upon the plurality of overlap hypotheses for each possible match combination, and comparing the co-occurrence matrix scores to thereby reduce the number of possible match combinations. The processor may also perform a match operation for the sensed finger data set based upon the reduced number of possible match combinations. The sensed finger data set may include a sensed finger ridge flow data set, and each enrolled finger data set may include an enrolled finger ridge flow data set.
Finger Sensing Apparatus Using Template Watermarking And Associated Methods
Michael Boshra - Indialantic FL, US Jeffrey C. Lee - Melbourne FL, US Gregory Thomas Minteer - Indian Harbour Beach FL, US Gary S. Porter - Malabar FL, US Andrew J. Vandamia - Rockledge FL, US James R. Waldron - Oviedo FL, US
Assignee:
AuthenTec, Inc. - Melbourne FL
International Classification:
G06K 9/00
US Classification:
382124
Abstract:
A finger sensing apparatus may include an integrated circuit (IC) substrate, an array of finger sensing elements on the IC substrate, match circuitry on the IC substrate, and a host platform external from the IC substrate and cooperating with the array of finger sensing elements for generating finger template data with a template watermark embedded therein. The host platform may also generate a match score based on the finger template data with the template watermark embedded therein for use by the match circuitry.
Finger Sensing Apparatus Using Hybrid Matching And Associated Methods
Michael BOSHRA - Indialantic FL, US Robert Scott Brandt - Satellite Beach FL, US Jeffrey C. Lee - Melbourne FL, US Gregory Thomas Minteer - Indian Harbour Beach FL, US Gary S. Porter - Malabar FL, US Peter E. Sherlock - Viera FL, US Andrew J. Vandamia - Rockledge FL, US James R. Waldron - Oviedo FL, US
Assignee:
AuthenTec, Inc. - Melbourne FL
International Classification:
G06K 9/00
US Classification:
382124
Abstract:
A finger sensing apparatus may include a finger sensor including an integrated circuit (IC) substrate, an array of finger sensing elements on the IC substrate, and match circuitry on the IC substrate for performing final finger matching. The finger sensing apparatus may also include a host platform cooperating with the array of finger sensing elements for performing at least one finger prematch function. In addition, the finger sensor and the host platform may implement at least one security function therebetween. The at least one security function may include a watermarking function, and/or an encryption/decryption function.
Real Estate Brokers
Michael Boshra, Egyptian Hills Paradise Valley Agent
Apple - Melbourne, Florida Area since Oct 2012
Senior Algorithm Scientist
AuthenTec Nov 1999 - Oct 2012
Senior Principal Algorithms Engineer
University of California, Riverside Aug 1997 - Sep 1999
Post-Doctoral Fellow
University of Alberta Sep 1992 - Jul 1997
Graduate Assistant
Systems Research Egypt Sep 1989 - Aug 1992
Analyst Programmer
Education:
University of Alberta 1992 - 1997
Ph.D, Computing Science
Alexandria University 1992
M.Sc, Computer Science
Alexandria University 1988
B.Sc. (honors), Computer Science
Skills:
Pattern Recognition Computer Vision Biometrics Image Processing Machine Learning Algorithms Robotics Software Development C++ Programming Software Engineering Object Oriented Design Simulations Sensors System Architecture Software Design C Signal Processing Embedded Software Integration Embedded Systems Algorithm Design Digital Signal Processors