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PTZ Camera Assisted Face Acquisition, Tracking & Recognition

Face recognition systems typically have a rather short operating distance with standoff (distance between the camera and the subject) limited to 12 meters. When these systems are used to capture face images at a larger distance (to 10 m), the resulting images contain only a small number of pixels on the face region, resulting in a degradation in face recognition performance. To address this problem, we propose a camera system consisting of one PTZ camera and two static cameras to acquire high resolution face images up to a distance of 10 meters. We propose a novel camera calibration method based on the coaxial configuration between the static and PTZ cameras. We also use a linear prediction model and camera control to mitigate delays in image processing and mechanical camera motion. The proposed system has a larger standoff in face image acquisition and effectiveness in face recognition test. Experimental results on video data collected at a distance ranging from 5 to 10 meters of 20 different subjects as probe and 10,020 subjects as gallery shows 96% rank-1 identification accuracy compared to 0.1% rank-1 accuracy of the conventional camera system using a state-ofthe-art matcher

PILL-ID: Matching and Retrieval of Drug Pill Imprint Images

Drug pill matching and retrieval is an important problem due to an increase in the number of tablet type illicit drugs being circulated in our society. We propose an automatic method to match drug pill images based on the imprints appearing on the tablet. This will help identify the source and manufacturer of the illicit drugs. The feature vector extracted from tablet images is based on edge localization and invariant moments. Instead of storing a single template for each pill type, we generate multiple templates during the edge detection process. This circumvents the difficulties during matching due to variations in illumination and viewpoint. Experimental results using a set of real drug pill images (822 illicit drug pill images and 1,294 legal drug pill images) showed 76.7% (93%) rankone (rank-20) matching accuracy.

Age Invariant Face Recognition

One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations such as pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.