Abstract: A system for providing stand-off biometric verification of a driver of a vehicle at a control gate while the vehicle is moving, including 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 data from an RFID vehicle tag of the vehicle that is trying to pass through the gate. The RFID personal tag reader reads data 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. All the data and facial images detected by the reader are sent to a computer for further processing (final face verification).

Abstract: Thermal infrared image data of at least a region of a face of a person in an enclosure is provided. The enclosure, for example, may include a first enclosed volume and a second enclosed volume physically separated from the first enclosed volume. The first enclosed volume may include an entrance door sized to allow a person to enter the first enclosed volume. The enclosure provides a controlled environment for performing measurements (e.g., capturing thermal infrared image data) for use in determining a physiological state of a person (e.g., anxiety, deception, etc.).

Abstract: 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.

Abstract: A knowledge and model based adaptive signal processor for ensuring optimum performance in automated object recognition (AOR) systems. The invention does not require ground-truth information but uses predictive performance models for parameter adaption and updates for the AOR. The performance models are representations of continuous surfaces that interpolate and extrapolate for data and scenarios not previously tested. The present invention increases probability of detection and decreases false detection alarm rates of AOR systems.