Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

Iris, fingerprint, and three-dimensional face recognition technologies used in mobile devices face obstacles owing to price and size restrictions by additional cameras, lighting, and sensors. As an alternative, two-dimensional face recognition based on the built-in visible-light camera of mobile devices has been widely used. However, face recognition performance is greatly influenced by the factors, such as facial expression, illumination, and pose changes. Considering these limitations, researchers have studied palmprint, touchless fingerprint, and finger-knuckle-print recognition using the built-in visible light camera. However, these techniques reduce user convenience because of the difficulty in positioning a palm or fingers on the camera. To consider these issues, we propose a biometric system based on a finger-wrinkle image acquired by the visible-light camera of a smartphone. A deep residual network is used to address the degradation of recognition performance caused by misalignment and illumination variation occurring during image acquisition. Owing to the unavailability of the finger-wrinkle open database obtained by smartphone camera, we built the Dongguk finger-wrinkle database, including the images from 33 people. The results show that the recognition performance by our method exceeds in those of conventional methods.