Person authentication using 3D palmprints

Biometric technologies, including fingerprint, face, iris, and palmprint recognition, are drawing increasing attention in both academic research and industrial applications.1 Palmprint identification uses the inner surface of the palm to distinguish between people2 and includes among its many merits stable features, a high level of accuracy, and user-friendliness. To the best of our knowledge, all of the current palmprint recognition methods are 2D: they employ a digital camera to capture a 2D image and then extract features such as principle lines and wrinkles. However, a 2D palmprint could be relatively easy to forge, since it only shows the texture information of the palm surface. Recently, 3D techniques have been applied to biometric authentication, for example, in face3 and ear recognition.4 The human palm, of course, is also a 3D object and offers plenty of data that might be used for authentication. Moreover, unlike the face and ear, features of a palm such as principle lines and wrinkles are especially resistant to counterfeiting owing to variations in their depth. By the same token, they are harder to acquire in 3D mode. The most popular approach to generating the desired depth information is laser triangulation.3, 4 But this method, too, falls short. To achieve more accurate and higher acquisition speed than with ordinary laser-scanning technologies, we have developed a 3D acquisition device based on a structured light method5 that uses a cost-effective gray liquid crystal display (LCD) projector with a light-emitting diode (LED) light source to project shift-structured light patterns (stripes) onto the palm surface. The reflected light is captured by a charge coupled device (CCD) camera, and a series of images are then collected that can be used for calculating the palm depth information. Figure 1 shows a 3D palmprint obtained using our device. In our approach, we first obtain a 3D palmprint from which we extract the region of interest (ROI) to eliminate noisy data on the boundary and reduce computing time. Collecting the 3D information also produces a 2D palmprint. We apply 2D Figure 1. A 3D palmprint sample.