This paper presents the largest inertial sensor-based gait database in the world, which is made open to the research community, and its application to a statistically reliable performance evaluation for gait-based personal authentication. We construct several datasets for both accelerometer and gyroscope of three inertial measurement units and a smartphone around the waist of a subject, which include at most 744 subjects (389 males and 355 females) with ages ranging from 2 to 78 years. The database has several advantages: a large number of subjects with a balanced gender ratio, variations of sensor types, sensor locations, and ground slope conditions. Therefore, we can reliably analyze the dependence of gait authentication performance on a number of factors such as gender, age group, sensor type, ground condition, and sensor location. The results with the latest existing authentication methods provide several insights for these factors.
Abstract. The range of scene depths that appear focused in an image is known as the depth of field (DOF). Conventional cameras are limited by a fundamental trade-off between depth of field and signal-to-noise ratio (SNR). For a dark scene, the aperture of the lens must be opened up to maintain SNR, which causes the DOF to reduce. Also, today's cameras have DOFs that correspond to a single slab that is perpendicular to the optical axis. In this paper, we present an imaging system that enables one to control the DOF in new and powerful ways. Our approach is to vary the position and/or orientation of the image detector, during the integration time of a single photograph. Even when the detector motion is very small (tens of microns), a large range of scene depths (several meters) is captured both in and out of focus. Our prototype camera uses a micro-actuator to translate the detector along the optical axis during image integration. Using this device, we demonstrate three applications of flexible DOF. First, we describe extended DOF, where a large depth range is captured with a very wide aperture (low noise) but with nearly depth-independent defocus blur. Applying deconvolution to a captured image gives an image with extended DOF and yet high SNR. Next, we show the capture of images with discontinuous DOFs. For instance, near and far objects can be imaged with sharpness while objects in between are severely blurred. Finally, we show that our camera can capture images with tilted DOFs (Scheimpflug imaging) without tilting the image detector. We believe flexible DOF imaging can open a new creative dimension in photography and lead to new capabilities in scientific imaging, vision, and graphics.
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