I would like to use smartphone as a finger print scanner, real time using smart phone that able to detect finger (same as now face recognition) and refocus the camera for a clear shot of the finger.
It will be faster if you set the focus to the shallowest depth and instruct the user to bring the finger slowly towards to lens. While the finger is moving, you can track the finger tip and check the contrast of every frame. When the contrast is maximum, then programmetically capture the photo.
The finger can be easily detected by using a simple blob detection algorithm. Process the captured image to enhance the ridges and the use minutiae-based fingerprint matching for the recognition.
This paper details the development of a smartphone based online system to automatically identify a person by using their finger knuckle image. The key objective is to exploit user-friendly biometric, with least privacy concern, to enhance security of the data in smartphone. The final product from this research is a finger knuckle authentication smartphone application, which is developed under Android operating system with environment version 2.3.3. This paper has developed some specialized algorithms for the finger knuckle detection, image pre-processing and region segmentation. Automatically detected and segmented finger knuckle images are used to encode finger knuckle pattern phase information using a pair of log-Gabor filters. Efficient implementation of various modules is achieved in C/C++ programming language, with OpenCV library, for online application. We also developed a user-friendly graphical user interface for the users to enroll and authenticate themselves. The developed system can therefore acquire finger knuckle image from the smartphone camera and automatically authenticate the genuine users. This paper has also developed a new smartphone based finger knuckle image database of 561 finger knuckle images of 187 different fingers from 109 users, in real imaging environment. In the best of our knowledge, this is the first attempt to develop a mobile phone based finger knuckle identification which has shown highly promising results in automatically identifying the users from their finger knuckle images