Abstract
Biometrics usage is growing daily and fingerprint-based recognition system is among the most effective and popular methods of personality identification. The conventional fingerprint sensor functions on total internal reflectance (TIR), which is a method that captures the external features of the finger that is presented to it. Hence, this opens it up to spoof attacks. Liveness detection is an anti-spoofing approach that has the potentials to identify physiological features in fingerprints. It has been demonstrated that spoof fingerprint made of gelatin, gummy and play-doh can easily deceive sensor. Therefore, the security of such sensor is not guaranteed. Here, we established a secure and robust fake-spoof fingerprint identification algorithm using Circular Gabor Wavelet for texture segmentation of the captured images. The samples were exposed to feature extraction processing using circular Gabor wavelet algorithm developed for texture segmentations. The result was evaluated using FAR which measures if a user presented is accepted under a false claimed identity. The FAR result was 0.03125 with an accuracy of 99.968% which showed distinct difference between live and spoof fingerprint.  Â
Highlights
Liveness detection is the ability of a system to detect if a biometric sample offered to it is live or otherwise [1,2]
Any system intended to safeguard against artificial fingerprints attacks must be able to predict if biometric sample offered to it belongs to an active human being that was initially registered in the system
We developed a secure and better fake-proof fingerprint identification algorithm for fingerprint texture segmentation using Circular Gabor Wavelet Transform for liveness discovery purpose
Summary
Liveness detection is the ability of a system to detect if a biometric sample offered to it is live or otherwise [1,2]. Extra hardware can be used to obtain life signs; information already captured with the aid of the system can be utilized to identify signs of life, liveness information embedded in the biometric can be used and texture information presented to the sensor can be employed [4]. Some of these methods are faced with problems such as high cost of implementation, difficulty in extracting life signs without using additional hardware.
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