System and method for using three dimensional infrared imaging to identify individuals

• US 8,494,227 B2
• Filed: 04/15/2008
• Issued: 07/23/2013
• Est. Priority Date: 04/17/2007
• Status: Expired due to Fees

• Current Assignee: Francine J. Prokoski
• Original Assignee: Francine J. Prokoski
• Inventors: Prokoski, Francine J.
• Primary Examiner(s): CHAWAN, SHEELA C
• Application Number:
  US12/596,635
• Publication Number:
  US 20100189313A1
• Time in Patent Office: 1,925 Days
• Field of Search: 705/1, 705/3, 600/443, 600/921, 707 1- 6, 707/10, 707/705, 707/E17.001, 707/E17.019, 707/E17.02, 707/E17.023, 707/E17.021, 707/999.1, 707/999.103, 707/999.104, 707/999.006, 715/700, 715/764, 715/810, 715/835, 250/216, 250/221, 250/557, 250/208.1, 250/555, 382/115, 382/118, 382/125, 382/100, 382/276, 382/305, 382/181, 382/190, 382/195, 382/209, 382/217, 382/218, 382/220, 382/254, 382/270, 382/228, 382/224, 382/312, 382/313, 382/154, 358/1.1, 358/1.9
• US Class Current: 382/115
• CPC Class Codes: A61B 5/0064 Body surface scanning
• CPC Class Codes: A61B 5/0086 using infrared radiation
• CPC Class Codes: A61B 5/015 By temperature mapping of body part
• CPC Class Codes: A61B 5/1077 Measuring of profiles
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/415 the glands, e.g. tonsils, adenoids or thymus
• CPC Class Codes: A61B 5/418 lymph vessels, ducts or nodes
• CPC Class Codes: A61B 5/445 Evaluating skin irritation or skin trauma, e.g. rash, eczema, wound, bed sore
• CPC Class Codes: A61B 5/4519 Muscles A61B5/389, A61B5/224 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G01K 13/20 Clinical contact thermometers for use with humans or animals
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

Calibrated infrared and range imaging sensors are used to produce a true-metric three-dimensional (3D) surface model of any body region within the fields of view of both sensors. Curvilinear surface features in both modalities are caused by internal and external anatomical elements. They are extracted to form 3D Feature Maps that are projected onto the skin surface. Skeletonized Feature Maps define subpixel intersections that serve as anatomical landmarks to aggregate multiple images for models of larger regions of the body, and to transform images into precise standard poses. Features are classified by origin, location, and characteristics to produce annotations that are recorded with the images and feature maps in reference image libraries. The system provides an enabling technology for searchable medical image libraries.

Claims

1. A computer implemented method for identifying a person, comprising:

generating by a computer a body map of one or more body segments of the person;

comparing by the computer the body map of said one or more body segments to body maps of corresponding segments of known persons; and

applying by the computer a threshold test to determine whether one or more body maps of corresponding segments of known persons is a match, wherein each body map is generated by;

collecting simultaneous images of the body segment using a plurality of imaging devices;

combining corrected and overlaid images of the infrared imager and the range imager to produce a 3D infrared model;

processing the range image to extract a curvilinear feature map of external anatomy;

processing the infrared image to extract a curvilinear feature map of internal anatomy;

skeletonizing the respective curvilinear feature maps;

producing skeleton node maps containing intersection and branch locations of said curvilinear features;

labeling each node according to a standard directory description of intersecting or branching anatomical features;

forming a layered composite image of the infrared, range, and visual images, plus their feature maps, plus their skeletonized feature maps, plus their node maps;

selecting nodes corresponding to three reference points designated for said body segment;

rotating the composite image in three-dimensional space such that the three reference points define a two dimensional (2D) image plane, said 2D image plane being a standard pose for said body segment;

storing said rotated standardized composite image as a body map.

2. A method of identification as in claim 1, wherein the body map is of the face area, further comprising:

performing locally adaptive filtering on the infrared layer of the body map composite to enhance visualization of internal anatomical structures including blood vessels;

thresholding the enhanced infrared image to produce binary curvilinear features;

skeletonizing the features to produce single-pixel line widths; and

calculating measurements of skeletonized features including number of features, total length of line features, distribution of line feature lengths, and distribution of feature angular orientation,

wherein said set of feature measurements from the facial body map of the person and the known person are used in the threshold test.

3. A method of identification as in claim 2, further comprising:

creating a node map showing intersections and branch points in the facial body map;

determining distribution of nodes for the facial body map in a standard pose (frontal or profile) with respect to bilateral symmetry, number and type of node in each segment of the face;

determining (x,y,z) location relative to a body-centric coordinate system and type for each node;

wherein said set of node measurements is used in the threshold test.

4. A method of identification as in claim 3, wherein said set of node measurements includes vector angles of features intersecting or branching to form each node location.

5. A method of identification as in claim 3, wherein said set of node measurements includes range value at each node location.