Honeywell International Inc.

Honeywell G.m.b.h.

Michael E Bazakos

Yunqian Ma

Vassilios Morellas

David W Meyers

Kwong Wing Au

Rida M Hamza

George A Kilgore

Andrew H Johnson

Murray J Cooper

Ioannis Pavlidis

Hatem N Nasr

Firooz A Sadjadi

Hossien Amehdi

Vassailios Morellas

Yungian Ma

Vassillos Morellas

Johnson Andrew

A stand-off range or at-a-distance iris detection and tracking for iris recognition having a head/face/eye locator, a zoom-in iris capture mechanism and an iris recognition module. The system may obtain iris information of a subject with or without his or her knowledge or cooperation. This information may be sufficient for identification of the subject, verification of identity and/or storage in a database.

System for iris detection, tracking and recognition at a distance

System for iris detection tracking and recognition at a distance

In an embodiment, one or more sequences of learning video data is provided. The learning video sequences include an action. One or more features of the action are extracted from the one or more sequences of learning video data. Thereafter, a sequence of operational video data is received, and the one or more features of the action from the sequence of operational video data is extracted. A comparison is then made between the extracted one or more features of the action from the one or more sequences of learning video data and the one or more features of the action from the sequence of operational video data. In an embodiment, this comparison allows the determination of whether the action is present in the operational video data.

Automated activity detection using supervised learning

A system for providing stand-off biometric verification of a driver of a vehicle while the vehicle is moving and/or a person on foot at a control gate, including an RFID vehicle tag reader, an RFID personal smart card reader and a facial detection and recognition (verification) system. The driver carries a RFID personal smart card that stores personal information of the driver and a face template of the driver. The vehicle carries a RFID vehicle tag that stores information regarding the vehicle. When the vehicle approaches the control gate, the RFID vehicle tag reader reads data from the RFID vehicle tag and the RFID personal tag reader reads data from the RFID personal smart card. The facial detection and verification system scans and reads a facial image for the driver. All the data and facial images detected by the readers are sent to a local computer at the control gate for further processing (final face verification). The local computer at the control gate decodes and retrieves the face template from the data read from the RFID personal smart card.

Distributed stand-off verification and face recognition systems (FRS)

Distributed stand-off ID verification compatible with multiple face recognition systems (FRS)

Facial detection and tracking systems and methods within a wide field of view are disclosed. A facial detection and tracking system in accordance with an illustrative embodiment of the present invention can include a wide field of view camera for detecting and tracking one or more objects within a wider field of view, and at least one narrower field of view camera for obtaining a higher-resolution image of each object located within a subset space of the wider field of view. The wide field of view camera can be operatively coupled to a computer or other such device that determines the subset space location of the individual within the wider field of view, and then tasks one or more of the narrower field of view cameras covering the subset space location to obtain a high-resolution image of the object.

Face detection and tracking in a wide field of view

A system for tracking objects across an area having a network of cameras with overlapping and non-overlapping fields of view. The system may use a combination of color, shape, texture and/or multi-resolution histograms for object representation or target modeling for the tacking of an object from one camera to another. The system may include user and output interfacing.

Object tracking system

A multi-spectral imaging surveillance system and method in which a plurality of imaging cameras is associated with a data-processing apparatus. A module can be provided, which resides in a memory of said data-processing apparatus. The module performs fusion of a plurality images respectively generated by varying imaging cameras among said plurality of imaging cameras. Fusion of the images is based on a plurality of parameters indicative of environmental conditions in order to achieve enhanced imaging surveillance thereof. The final fused images are the result of two parts: an image fusion portion, and a knowledge representation part. For the final fusion, many operators can be utilized, which can be applied between the image fusion result and the knowledge representation portion.

Multi-spectral fusion for video surveillance

Methods and systems for the unsupervised learning of events contained within a video sequence, including apparatus and interfaces for implementing such systems and methods, are disclosed. An illustrative method in accordance with an exemplary embodiment of the present invention may include the steps of providing a behavioral analysis engine, initiating a training phase mode within the behavioral analysis engine and obtaining a feature vector including one or more parameters relating to an object located within an image sequence, and then analyzing the feature vector to determine a number of possible event candidates. The behavioral analysis engine can be configured to prompt the user to confirm whether an event candidate is a new event, an existing event, or an outlier. Once trained, a testing/operational phase mode of the behavioral analysis engine can be further implemented to detect the occurrence of one or more learned events, if desired.

Unsupervised learning of events in a video sequence

A face detection and recognition system having several arrays imaging a scene in the infrared and visible spectrums. The system may use weighted subtracting and thresholding to distinguish human skin in a sensed image. A feature selector may locate a face in the image. The image may be cropped with a frame or border incorporating essentially only the face. The border may be superimposed on images from an infrared imaging array and the visible imaging array. Sub-images containing the face may be extracted from within the border on the infrared and visible images, respectively, and compared with a database of face information to attain recognition of the face. Confidence levels of recognition for infrared and visible imaged faces may be established. A resultant confidence level of recognition may be determined from these confidence levels. Infrared lighting may be used as needed to illuminate the scene.

Infrared and visible fusion face recognition system

An image is processed by a sensed-feature-based classifier to generate a list of objects assigned to classes. The most prominent objects (those objects whose classification is most likely reliable) are selected for range estimation and interpolation. Based on the range estimation and interpolation, the sensed features are converted to physical features for each object. Next, that subset of objects is then run through a physical-feature-based classifier that re-classifies the objects. Next, the objects and their range estimates are re-run through the processes of range estimation and interpolation, sensed-feature-to-physical-feature conversion, and physical-feature-based classification iteratively to continuously increase the reliability of the classification as well as the range estimation. The iterations are halted when the reliability reaches a predetermined confidence threshold. In a preferred embodiment, a next subset of objects having the next highest prominence in the same image is selected and the entire iterative process is repeated. This set of iterations will include evaluation of both of the first and second subsets of objects. The process can be repeated until all objects have been classified.

Method and apparatus for identifying physical features in video

A system and method detects the intent and/or motivation of two or more persons or other animate objects in a video scene. In one embodiment, the system forms a blob of the two or more persons, draws a bounding box around said blob, calculates an entropy value for said blob, and compares that entropy value to a threshold to determine if the two or more persons are involved in a fight or other altercation.

Detection of abnormal crowd behavior

A face detection and recognition system having several arrays imaging a scene at different bands of the infrared spectrum. The system may use weighted subtracting and thresholding to distinguish human skin in a sensed image. A feature selector may locate a face in the image. The face may be framed or the image cropped with a frame or border to incorporate essentially only the face. The border may be superimposed on an image direct from an imaging array. A sub-image containing the face may be extracted from within the border and compared with a database of face information to attain recognition of the face. A level of recognition of the face may be established. Infrared lighting may be used as needed to illuminate the scene.

Infrared face detection and recognition system

A checkpoint screening system uses multiple cameras to provide images at different angles of a person to be screened. Different features extracted from the images at different angles are compared to a profile associated with the person to be screened. In one embodiment, the person first provides an ID, such as a drivers license or other identification, and the profile is retrieved. If a match is detected, the person may continue through the checkpoint. If no match is detected, the person may be directed to a different path through the checkpoint for further verification of identity. A registration process is used to enroll members and obtain a member profile. Three camera angles are utilized to provide a three dimensional image. Each image is independently compared against the profile and the decisions from such comparisons are weighted. The profile is regressively updated and weighted if a match is detected.

Face identification verification using 3 dimensional modeling

A system for providing stand-off biometric verification of a driver of a vehicle at a control gate while the vehicle is moving, including a pre-verification system and a post-verification systems. The pre-verification system is installed before an entrance of a facility and comprises an RFID vehicle tag reader, an RFID personal tag reader and a facial detection and recognition (verification) system. The RFID vehicle tag reader scans and reads an ID from an RFID vehicle tag of the vehicle that is trying to pass through the gate. The RFID personal tag reader reads an ID from an RFID personal tag carried by personnel who are driving in the vehicle. The facial detection and verification system scans and reads facial images for the driver. The post-verification system is installed on at least one of an entrance and an exit for post-verification to ensure that the vehicle that enters the entrance or leaves from the exit is the one that has been verified/denied at the control gate. In one embodiment, the post-verification system comprises an RFID personal tag reader and an RFID vehicle tag reader. In another embodiment, the post-verification system also comprises a facial detection and recognition system.

Growing community of inventors

Bloomington, MN, United States of America

Michael E Bazakos