Patents by Inventor Joshua J. ENGELSMA

Joshua J. ENGELSMA has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Fixed length fingerprint representation
    Patent number: 11373438
    Abstract: A computer-implemented method for generating a representation for a fingerprint includes receiving, by a computer processor, an image of a given fingerprint. The method extracts particular attributes for the given fingerprint from the image using a first neural network. The first neural network is trained to identify particular attributes in fingerprints and constructs a first feature vector from the extracted particular attributes, where the first feature vector has a first fixed length. The method includes extracting textural features of the given fingerprint from the image using a second neural network, where the second neural network is trained to identify textural features that are not limited to particular attributes and constructing a second feature vector from the extracted textural features, where the second feature vector has a second fixed length. The method includes concatenating the first feature vector with the second feature vector to form a representation for the given fingerprint.
    Type: Grant
    Filed: February 10, 2020
    Date of Patent: June 28, 2022
    Assignee: Board of Trustees of Michigan State University
    Inventors: Joshua J. Engelsma, Kai Cao, Anil K. Jain
  • FIXED LENGTH FINGERPRINT REPRESENTATION
    Publication number: 20210365532
    Abstract: A computer-implemented method for generating a representation for a fingerprint includes receiving, by a computer processor, an image of a given fingerprint. The method extracts particular attributes for the given fingerprint from the image using a first neural network. The first neural network is trained to identify particular attributes in fingerprints and constructs a first feature vector from the extracted particular attributes, where the first feature vector has a first fixed length. The method includes extracting textural features of the given fingerprint from the image using a second neural network, where the second neural network is trained to identify textural features that are not limited to particular attributes and constructing a second feature vector from the extracted textural features, where the second feature vector has a second fixed length. The method includes concatenating the first feature vector with the second feature vector to form a representation for the given fingerprint.
    Type: Application
    Filed: February 10, 2020
    Publication date: November 25, 2021
    Applicant: Board of Trustees of Michigan State University
    Inventors: Joshua J. ENGELSMA, Kai CAO, Anil K. JAIN