sources of error begin with insufficient distinguishing detail in the biometric identifiers themselves (such as faint fingerprint ridges) and extend to variability in their presentations to a sensing instrument (which, depending on what is being measured and how, may result from injury, changing lighting conditions, or the aging process). The capture of the biometric identifiers by the sensors is affected both by the human interaction with the sensor (such as assisted vs. nonassisted sample capture and cooperative vs. noncooperative system users) and by the precision of the acquisition device itself. The quality of the information extracted from the sensor and used in the subsequent matching process can vary as well. The metric used in the matching process to measure similarities may be faulty or lack sufficient information to determine a match or a mismatch. Furthermore, to understand how the various stages of the data acquisition and processing sequence affect the end performance of the biometric system and how they can be improved, each stage needs to be modeled independently as well as in different system architectures.
To increase the information captured by the biometric system and to facilitate matching, including the ability to discriminate between genuine and imposter matches, participants suggested that sensor improvements could involve higher resolution and a higher signal-to-noise ratio (SNR). In addition, sensors that collect multiple biometrics within one device or system may offer improvements with respect to a range of characteristics such as accuracy and efficiency and accommodation of a broader population. Current research in this area aims to make multiple low-resolution images from video surveillance systems usable for facial recognition. An example was given of a system that uses several different cameras to track the location of a person in a room, with one of the cameras controlled by the location of the person’s head. Such a system can log all the people who have been in a room and capture the frontal images that are best suited to facial recognition at higher resolution.
Given that users of biometric systems may not be familiar with the technology, the ergonomics of the sensor and associated data capture hardware may affect the biometric information that is collected. To improve results, some participants suggested that user interaction must be either intuitive or minimized to the point that there is little, or no, interaction with the acquisition device. The prototype discussed at the workshop for on-the-move iris recognition allows iris images to be captured while the individual is walking past the sensor. This approach aims to minimize the acquisition constraints by expanding the standoff distance, or the distance of the acquisition system and the camera illumination from the subject, and the capture volume, or the area in which the biometric may be captured within a particular length of time.2 However, additional improvements in algorithms will be required to further minimize these constraints as well as reduce orientation requirements that currently require a direct gaze into the camera.
Continued algorithm development and better fusion of biometrics will generate more information to aid in the matching process. Though algorithms are continuing to improve and to process more information, additional research will be needed in coping with the variability of information over time—a consequence of the human aging process—especially for the processing of children and particularly for new biometric modes, such as three-dimensional facial recognition. For biometric fusion, panelists suggested potentially good combinations, such as face and finger, finger
The performance of the off-the-shelf iris recognition system described at the workshop, the LG-3000, includes a standoff of 10 cm, a capture volume of 0.04 liters, or 2 cm * 2 cm * 10 cm, within 3-10 seconds, and requires stationary use. In contrast, the on-the-move iris recognition prototype increases the camera standoff to 3 m and illumination by 1 m, capture volume expands to 10 liters, or 60 cm * 30 cm * 5 cm, within 0.05 seconds, and permits a walking speed of 1 m per second.