There is a critical need to conduct operational interviews in a wide range of interview and assessment situations, including conventional structured interviews as well as cases in which subjects are unconstrained. Current progress of three advanced prototype instrument development projects looking at non-contact sensing of human physiology to determine the veracity of human communications are presented. These include: 1) Thermal Facial Screening (TFS); 2) Turnkey Remote Assessment of Concealed Knowledge using Eye movement Recordings (TRACKER); and 3) Laser Doppler Vibrometry (LDV). Signals are measured with superior technical quality, in comparison to those obtained with conventional contact methods. Depending on the operational need and the specific context, these instruments can be used as stand-alone techniques or integrated into a multi-modal evaluation of human credibility. Thus, a comprehensive assessment using multiple physiological response systems is possible. A description each technique, the current state of these research efforts, and an overview of the potential for each of these emerging technologies will be provided.
Due to the modern advent of near ubiquitous accessibility to rapid international transportation the epidemiologic trends of highly communicable diseases can be devastating. With the recent emergence of diseases matching this pattern, such as Severe Acute Respiratory Syndrome (SARS), an area of overt concern has been the transmission of infection through respiratory droplets. Approved facemasks are typically effective physical barriers for preventing the spread of viruses through droplets, but breaches in a mask’s integrity can lead to an elevated risk of exposure and subsequent infection. Quality control mechanisms in place during the manufacturing process insure that masks are defect free when leaving the factory, but there remains little to detect damage caused by transportation or during usage. A system that could monitor masks in real-time while they were in use would facilitate a more secure environment for treatment and screening. To fulfill this necessity, we have devised a touchless method to detect mask breaches in real-time by utilizing the emissive properties of the mask in the thermal infrared spectrum. Specifically, we use a specialized thermal imaging system to detect minute air leakage in masks based on the principles of heat transfer and thermodynamics. The advantage of this passive modality is that thermal imaging does not require contact with the subject and can provide instant visualization and analysis. These capabilities can prove invaluable for protecting personnel in scenarios with elevated levels of transmission risk such as hospital clinics, border check points, and airports.
Considerable progress has been made in face recognition research
over the last decade especially with the development of powerful
models of face appearance (i.e., eigenfaces). Despite the variety
of approaches and tools studied, however, face recognition is not
accurate or robust enough to be deployed in uncontrolled
environments. Recently, a number of studies have shown that
infrared (IR) imagery offers a promising alternative to visible
imagery due to its relative insensitive to illumination changes.
However, IR has other limitations including that it is opaque to
glass. As a result, IR imagery is very sensitive to facial
occlusion caused by eyeglasses. In this paper, we propose fusing
IR with visible images, exploiting the relatively lower
sensitivity of visible imagery to occlusions caused by eyeglasses.
Two different fusion schemes have been investigated in this study:
(1) image-based fusion performed in the wavelet domain and, (2)
feature-based fusion performed in the eigenspace domain. In both
cases, we employ Genetic Algorithms (GAs) to find an optimum
strategy to perform the fusion. To evaluate and compare the
proposed fusion schemes, we have performed extensive recognition
experiments using the Equinox face dataset and the popular method
of eigenfaces. Our results show substantial improvements in
recognition performance overall, suggesting that the idea of
fusing IR with visible images for face recognition deserves
In the present paper we describe a novel method for scoring polygraph tests using thermal image analysis. Our method features three stages: image acquisition, physiological correlation, and pattern classification. First, we acquire facial thermal imagery using an accurate mid-infrared camera. Then, we transform the raw thermal data to blood flow rate data through heat transfer modeling. Finally, we classify the subject as deceptive or non-deceptive based on the nearest-neighbor classification method. We perform our analysis on the periorbital area of the subjects’ faces. Our previous research has indicated that the periorbital area is the facial area affected the most from blood flow redistribution during anxious states.
We present promising experimental results from 18 subjects. We henceforth anticipate that thermal image analysis will play an increasingly important role in polygraph testing as an additional scoring channel. Our ultimate objective is to increase the accuracy and reliability of polygraph testing through the fusion of traditional invasive 1D physiological measurements with novel non-invasive 2D physiological measurements.
Face detection is an important prerequisite step for successful face
recognition. Face detection methods reported in the literature are far from perfect and deteriorate ungracefully where lighting conditions cannot be controlled. We propose a method that could potentially outperform state-of-the-art face detection methods in environments with dynamic lighting conditions. The approach capitalizes upon our near-IR skin and face detection methods reported elsewhere. It ascertains the existence of a face within a skin region by finding the eyes and eyebrows. The eye-eyebrow pairs are determined by extracting appropriate features from multiple near-IR bands. In this paper we introduce a novel feature extraction method we call dynamic integral projection. The method is relatively simple but highly effective because the processing is constrained within the skin region and aided by the near-IR phenomenology.
Law Enforcement personnel are faced with new challenges to rapidly assess the credibility of statements made by individuals in airports, border crossings, and a variety of environments not conducive to interviews. New technologies may offer assistance to law enforcement personnel in the interview and interrogation process. Additionally, homeland defense against terrorism challenges scientists to develop new methods of assessing truthfulness and credibility in humans. Current findings of four advanced research projects looking at emerging technologies in the credibility assessment are presented for discussion. This paper will discuss research efforts on four emerging technologies now underway at DoDPI and other institutions. These include: (1) Thermal Image Analysis (TIA); (2) Laser Doppler Vibrometry (LDV); (3) Eye Movement based Memory Assessment (EMMA); and (4) functional Magnetic Resonance Imaging (fMRI). A description each technique, the current state of these research efforts, and an overview of the potential for each of these emerging technologies will be provided.