DECEPTION DETECTION USING OCULOMOTOR MOVEMENTS

• US 20100324454A1
• Filed: 06/22/2010
• Published: 12/23/2010
• Est. Priority Date: 06/22/2009
• Status: Abandoned Application

• Current Assignee: University of Utah Research Foundation (State of Utah)
• Original Assignee: University of Utah Research Foundation (State of Utah)
• Inventors: Kircher, John C., Hacker, Douglas J., Cook, Anne E.
• Application Number:
  US12/820,488
• Publication Number:
  US 20100324454A1
• Time in Patent Office: Days
• US Class Current: 600/587
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/164 Lie detection
• 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: A61B 5/7267 involving training the classification device
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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

Embodiments of the present invention relate to a rapid, automated, and objective method for using oculomotor measures to determine whether a person is being truthful or deceitful. More specifically, embodiments of the present invention measure a plurality of oculomotor and behavioral dependent measures while a subject reads and responds to written items that are both related and unrelated to a suspected crime. The oculomotor and behavioral measures may include pupil diameter, response times, the number of fixations during reading, the time spent reading and rereading items, and the rate of eye blinks on the current and subsequent item. Several behavioral and oculomotor measures were diagnostic of deception, and a weighted combination of four of those variables correctly classified 84% of guilty and 89% of innocent subjects.

Claims

1. A computer-implemented method for assessing the veracity of a subject comprising:

presenting the subject with one or more sets of written test items relating to one or more issues of relevance;

presenting the subject with one or more sets of written test items relating to one or more neutral issues;

presenting the subject with one or more sets of written test items relating to one or more comparison issues;

monitoring, by a computing device, one or more dependent measures while the subject is reading and responding to said test items; and

analyzing, by the computing device, the variance of said dependent measures to assess veracity of said subject.

2. The method for assessing the veracity of a subject of claim 1, wherein the dependent measure is selected from the group consisting of response time, proportion wrong, number of fixations, first past duration, reread duration, pupil diameter, item blink rate and next item blink rate.

3. The method for assessing the veracity of a subject of claim 1, wherein a plurality of oculomotor dependent measures are analyzed to assess the veracity of said subject.

4. The method for assessing the veracity of a subject of claim 1, wherein the variance of a plurality of dependent measures as selected from Table 4 are analyzed to assess the veracity of said subject.

5. The method for assessing the veracity of a subject of claim 1, wherein the test items relating to issues of relevance, neutral issues and comparison issues comprises staggering the test items.

6. The method for assessing the veracity of a subject of claim 2, wherein a plurality of dependent measures are analyzed using a discriminant function and said discriminant function yields a sensitivity of at least 80%.

7. The method for assessing the veracity of a subject of claim 6, wherein a plurality of dependent measures are analyzed using a discriminant function and said discriminant function yields a specificity of at least 80%.

8. A computer-implemented method for assessing the veracity of a subject comprising:

presenting the subject with one or more sets of written test items relating to one or more issues of relevance;

presenting the subject with one or more sets of written test items relating to one or more neutral issues;

presenting the subject with one or more sets of written test items relating to one or more comparison issues;

wherein the subject is instructed to respond to the written test items as quickly and accurately as possible;

monitoring, by a computing device, one or more dependent measures while the subject is reading and responding to said test item; and

analyzing, by the computing device, the variance of said dependent measures together with the results from one or more independent variables to assess the veracity of said subject.

9. The method for assessing the veracity of a subject of claim 8, wherein the dependent measure is selected from the group consisting of response time, proportion wrong, number of fixations, first past duration, reread duration, pupil diameter, item blink rate and next item blink rate.

10. The method for assessing the veracity of a subject of claim 8, wherein a plurality of oculomotor dependent measures are analyzed to assess the veracity of said subject.

11. The method for assessing the veracity of a subject of claim 8, wherein the variance of a plurality of dependent measures as selected from Table 4 are analyzed to assess the veracity of said subject.

12. The method for assessing the veracity of a subject of claim 8, wherein the test items relating to issues of relevance, neutral issues and comparison issues comprises staggering the test items.

13. The method for assessing the veracity of a subject of claim 8, wherein a plurality of dependent measures are analyzed using a discriminant function and said discriminant function yields a sensitivity of at least 80%.

14. The method for assessing the veracity of a subject of claim 13, wherein a plurality of dependent measures are analyzed using a discriminant function and said discriminant function yields a specificity of at least 80%.

15. The method for assessing the veracity of a subject of claim 14, wherein a plurality of dependent measures and independent variables are analyzed using a discriminant function and said discriminant function yields a sensitivity of at least 80%.

16. The method for assessing the veracity of a subject of claim 15, wherein a plurality of dependent measures and independent variables are analyzed using a discriminant function and said discriminant function yields a specificity of at least 80%.

17. The method for assessing the veracity of a subject of claim 15, wherein only easy items are presented to the test subject.

18. The method for assessing the veracity of a subject of claim 17, wherein a plurality of dependent measures and independent variables are analyzed using a discriminant function and said discriminant function yields a accuracy of at least 80%.

19. A system for assessing the veracity of a subject comprising:

a display; and

a processor, wherein the processor is configured to;

present, on the display, the subject with one or more sets of written test items relating to one or more issues of relevance;

present, on the display, the subject with one or more sets of written test items relating to one or more neutral issues;

present, on the display, the subject with one or more sets of written test items relating to one or more comparison issues;

receive any responses from the subject related to the one or more issues of relevance, the one or more neutral issues, and the one or more comparison issues;

monitor one or more dependent measures while the subject is reading and responding to said test items; and

analyze the variance of said dependent measures to assess veracity of said subject.

20. The system of claim 19, wherein the one or more dependent measures are selected from the group consisting of response time, proportion wrong, number of fixations, first past duration, reread duration, pupil diameter, item blink rate and next item blink rate.