Abstract
Existing methods for iris, fingerprint, and 3D face recognition in mobile devices have constraints in terms of price and size owing to their use of additional cameras, lighting, and sensors. Additionally, visible light, camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print recognition are difficult to be used in mobile devices due to limitations in recognition performance and user inconvenience. In response to these problems, studies have been conducted on finger wrinkle recognition in mobile devices; however, image quality is often reduced by motion blurring caused by the movement of the camera or the user’s finger, thereby reducing recognition performance. This study proposes a method for restoring and recognizing motion-blurred finger wrinkle images based on a generative adversarial network and deep convolutional neural network. Experiments were performed using two types of finger wrinkle databases, which were custom-made from images of 33 people captured by smart phone cameras (Dongguk mobile finger wrinkle database versions 1 and 2, denoted as DMFW-DB1 and DMFW-DB2, respectively). The results demonstrated high restoration and recognition performance in comparison with the state-of-the-art methods.
Highlights
Biometrics refers to a technology that performs personal authentication using the unique features of the human body
To resolve the problem of reduced recognition performance due to motion blurring during finger wrinkle recognition, this study has proposed an image restoration method that is based on DeblurGAN and a deep convolutional neural network (CNN)-based recognition method that uses the resulting images as input
The DMFW-DB1 database was created from artificially blurred images and another database, DMFW-DB2, was created from images that include the motion blurring that occurs when images are captured by an actual camera
Summary
Biometrics refers to a technology that performs personal authentication using the unique features of the human body. Researchers have studied biometric technologies that use a variety of human features, and the scope of the field is expanding from traditionally-used features, such as fingerprints, faces, and irises, to palm prints and finger knuckles. The iris, finger print, and 3D face recognition methods that are used in existing mobile devices have price and size constraints owing to their use of additional cameras, lighting, and sensors. A visible light camera-based 2D face recognition, palm print recognition, touchless fingerprint recognition, and finger knuckle print. Researchers are studying finger wrinkle recognition using a visible light camera; this approach has problems with reduced recognition performance due to a motion blurring that is caused by movement of a camera and the fingers when the image is captured
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