Nec Corporation

Other

Ford Global Technolgoies, LLC

Michigan State University

Debayan Deb

Srimat T Chakradhar

Biplob Kumar Debnath

Kwaku O Prakah-Asante

Giuseppe Coviello

Krishnaswamy Venkatesh Prasad

Anil K Jain

Arun Ross

Rahul Parthe

Vishesh Mistry

A system for an intelligent machine learning (ML)-based system for 3-D modeling of unique controllable heads. The system includes a processor of a modeling server connected to a user's data node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive user-desired input data related to modeling of a controllable 3-D head from the user's data node; parse the user-desired input data to derive a feature vector including 3-D head modeling parameters; retrieve from a local database previous user-desired inputs to fine-tune the feature vector; provide the fine-tuned feature vector to a machine learning (ML) module configured to generate the controllable 3-D head model; receive the 3-D head model from the ML module; and render the controllable 3-D head model to the user's data node.

Machine learning-based modeling of unique controllable heads

A fingerprinting solution that uses neural network (NN) based trained Machine Learning (ML) modules in combination with traditional image processing for contactless fingerprint capture, liveness detection to rule out fake fingers, and fingerprint matching using a portable handheld device with integrated camera, thereby eliminating the need for a special device dedicated for fingerprinting. The trained NN modules detect the size and direction of fingers in the captured image, check if fingers are reversed in the image (thereby making nails visible), check if the thumb of the correct hand is captured, and generate fixed-length fingerprint templates for subsequent matching of fingerprints. Three dimensional (3D) depth map of a finger is used to bring the fingerprint resolution to 500 dpi and eliminate distortion caused by the curvature of the finger shape to improve accuracy while scaling and flattening a fingerprint image. The solution facilitates contactless-to-contactless as well as contactless-to-contact based fingerprint matching.

Contactless fingerprint capture using artificial intelligence and image processing on integrated camera systems

A passive authentication method includes, in response to receiving a requested action from a first user, obtaining a set of sensor data and categorizing first sensor data of the set of sensor data into a first modality of a set of modalities. The method includes, for the first modality of the set of modalities, determining a distance value by applying a first modality model to the first sensor data and comparing the distance value to a first verified value of the first user for the first modality. The method includes, based on the comparison, determining a first authentication decision of the distance value. The method includes, in response to the first authentication decision indicating the first sensor data corresponds to the first user, performing the requested action.

Integrated systems and methods for passive authentication

A method is provided for classifying objects. The method detects objects in one or more images. The method tags each object with multiple features. Each feature describes a specific object attribute and has a range of values to assist with a determination of an overall quality of the one or more images. The method specifies a set of training examples by classifying the overall quality of at least some of the objects as being of an acceptable quality or an unacceptable quality, based on a user's domain knowledge about an application program that takes the objects as inputs. The method constructs a plurality of first-level classifiers using the set of training examples. The method constructs a second-level classifier from outputs of the first-level automatic classifiers. The second-level classifier is for providing a classification for at least some of the objects of either the acceptable quality or the unacceptable quality.

Automatically filtering out objects based on user preferences

Methods and systems for image clustering include matching a new image to a representative image of a cluster. The new image is set as a representative of the cluster with a first time limit. The new image is set as a representative of the cluster with a second time limit, responsive to a determination that the new image has matched at least one incoming image during the first time limit.

Growing community of inventors

Okemos, MI, United States of America

Debayan Deb