Morphotrust Usa, LLC

Idemia Identity & Security USA LLC

Joseph R Mayer

Brian K Martin

Rein-Lien Hsu

Alexandru Bogdan

Timothy J Brown

James R Queenan

Mike DiNicola

Some implementations of the present disclosure are directed to a computer-implemented method that includes: detecting a plurality of characters from a photo of an object; generating a set of character-based features based on the plurality of characters; matching the set of character-based features with a template of feature sets obtained from known objects; based on a matching set of character-based features, establishing a matching transformation between the object in the photo and the template of feature sets; and projecting the matching transformation to the photo such that the object is segmented from the photo.

System and method for OCR based object registration

A method for detecting images of fraudulently generated or photocopied secure credential documents using texture analysis includes receiving, by one or more processors, an image of a secure credential document from a computer device. The one or more processors segment the image of the secure credential document into multiple regions. For each region of the multiple regions, the one or more processors extract local high-resolution texture features from the image of the secure credential document. The one or more processors generate a score based on the local high-resolution texture features using a machine learning model. The score is indicative of a likelihood that the image of the secure credential document is fraudulently generated or photocopied. The one or more processors transmit a message to a display device indicating that the image of the secure credential document is fraudulently generated or photocopied.

Detection of fraudulently generated and photocopied credential documents

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, in real-time from an image capture device, a video stream comprising images of a document by a computing device. The computing device provides, for display in an image preview window, the video stream overlaid with a graphical capture guide. In response to detecting a lighting artifact in at least one image of the video stream, the computing device modifies the graphical capture guide within the image preview window. The computing device captures one or more of the images of the document from the video stream.

Capturing digital images of documents

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a data capture application of a computing device that receives image data for an image of a document and uses the image data to capture content on the document while minimizing glare at the document. The data capture application receives user input for positioning an image token provided for display to a user of the data capture application. Positioning the image token causes adjustment of an attribute value of the image data. In response to receiving the user input the data capture application determines whether the attribute value of the image data satisfies a predefined attribute value. Based on the attribute value satisfying the predefined attribute value, the data capture application generates a composite image of the document using the image data received by the data capture application.

Process for capturing content from a document

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a first image of a document, the first image including first glare shown on a first portion of the document. Detecting, in the first image, the first glare shown on the first portion of the document. In response to detecting the first glare in the first image, obtaining a second image of the document, the second image including second glare shown on a second portion of the document. Generating, from the first image and the second image, a third image of the document that includes less glare than the first glare in the first image and less glare than the second glare in the second image.

Reduction of glare in imaging documents

A computer-assisted method to segment an image of an identification document that includes: accessing data encoding the image of the identification document that includes four curved corners connected by four edges; applying gradient filters to the image of the identification document to generate an edge map that reveals candidate edges that are substantially horizontal or vertical relative to borders of the identification document, the gradient filters including gradient maps that reveal horizontal as well as vertical alterations of image intensities; accessing data encoding models representing an expected range of curved corners, each model quantifying both the radial and angular extents of the curved corners; identifying combinations of candidate edges, each combination including four candidate edges that are connected at four corners; and selecting a combination of candidate edges by applying the models to each of the four corners formed by the four candidate edges of each identified combination.

System and method for segmenting ID cards with curved corners

A system and method for determining whether an eye image includes a left eye or a right eye image is described. After obtaining an eye image, the centers of the iris and pupil in the eye image are located. A horizontal displacement between the centers of the iris and pupil is determined. In addition, neural network classifiers are used to perform eye corner detection to identify inner and outer eye corners in the image. A determination is made as to whether the eye image includes a left eye image or a right eye image based on the detected eye corners and horizontal displacement.

Differentiating left and right eye images

A method and system for detecting eye corners using neural network classifiers is described. After an eye image is received, the eye image may be processed by at least two neural network classifiers including an inner eye corner neural network classifier and an outer eye corner neural network classifier. The neural network classifiers provide periocular information including a distance or coordinates of an eye corner location from a center of an iris of the eye, and an outcome of whether the eye corner is an inner eye corner or an outer eye corner. Output from the various neural network classifiers are combined to generate a decision on the location of eye corners in an eye image.

Detecting eye corners

A method and system for determining a roll angle in an eye image is described. A center of an iris, an inner eye corner, and an outer eye corner in an eye image may be determined. The eye image may be normalized and transformed to a polar eye image using the center of the iris as a reference for the transformation. A shift associated with the inner eye corner and a shift associated with the outer eye corner may be determined. The shifts associated with the inner eye corner and the outer eye corner may be indicators of an orientation of the eye image or roll angle, and may be used for subsequent eye image processing and matching.

Growing community of inventors

Passaic County, NJ, United States of America

Joseph R Mayer