KEYWORDS: Image quality, 3D image processing, 3D scanning, 3D acquisition, Scanners, 3D printing, Optical engineering, Sensors, Projection systems, Algorithm development
The use of fingerprints as a biometric is both the oldest mode of computer-aided personal identification and the most-relied-on technology in use today. However, current acquisition methods have some challenging and peculiar difficulties. For higher performance fingerprint data acquisition and verification, a novel noncontact 3-D fingerprint scanner is investigated, where both the detailed 3-D and albedo information of the finger is obtained. The obtained high-resolution 3-D prints are further converted into 3-D unraveled prints, to be compatible with traditional 2-D automatic fingerprint identification systems. As a result, many limitations imposed on conventional fingerprint capture and processing can be reduced by the unobtrusiveness of this approach and the extra depth information acquired. To compare the quality and matching performances of 3-D unraveled with traditional 2-D plain fingerprints, we collect both 3-D prints and their 2-D plain counterparts. The print quality and matching performances are evaluated and analyzed by using National Institute of Standard Technology fingerprint software. Experimental results show that the 3-D unraveled print outperforms the 2-D print in both quality and matching performances.
The aim of this study is to design and develop a wireless distributed pyroelectric infrared sensor system which can track multiple humans. By using TI’s micro-controller MSP430149 and RF transceiver TRF6901, we have implemented a prototype multiple human tracking system, which can track two people in both follow-up and crossover scenarios with average tracking errors less than 0.5 m. The proposed wireless distributed infrared sensor system can not only run as a stand alone inmate/patient monitoring system under all illumination conditions, but also serve as a complement for conventional video and audio human tracking systems.
One goal of our research is to make wireless distributed pyroelectric sensor nodes an alternative to the centralized infrared video sensors, with lower cost, lower detectability, lower power consumption and computation workload, and less privacy infringement. To improve the identification rate and the number of people that can be recognized, one-by-one or simultaneously, we employ multiple sensor nodes to leverage the performance of the distributed sensor system. By using multiple sensor nodes the proposed biometric modality can be extended to the higher-security applications of walker recognition, and facilitate multiple human tracking.
Structured light illumination refers to a scanning process of projecting a series of patterns such that, when viewed
from an angle, a camera is able to extract range information. Ultimately, resolution in depth is controlled by the number
of patterns projected which, in turn, increases the total time that the target object must remain still. By adding a second
camera sensor, it becomes possible to not only achieve wrap around scanning but also reduce the number of patterns
needed to achieve a certain degree of depth resolution. But a second camera also makes it possible to reconstruct 3-D
surfaces through stereo-vision techniques and triangulation between the cameras instead of between the cameras and the
projectors. For both of these two tasks, correspondence between points from two cameras is essential. In this paper, we
develop a new method to find the correspondence between the two cameras using both the phase information generated
by the temporal multiplexed illumination patterns and stereo triangulation. We also analyze the resulting
correspondence accuracy as a function of the number of structured patterns as well as the geometric position of projector
to cameras.
We design and develop a low-cost pyroelectric detector-based IR motion-tracking system. We study the characteristics of the detector and the Fresnel lenses that are used to modulate the visibility of the detectors. We build sensor clusters in different configurations and demonstrate their use for human motion tracking.
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