Wavelet-based multiresolution analysis of ridges for fingerprint liveness detection
Shankar Bhausaheb Nikam, Suneeta Agarwal · International Journal of Information and Computer Security · 2009
In this paper, a new wavelet-based perspiration detection algorithm is proposed for fingerprint liveness detection. It is based on processing time-series ridge lines in the wavelet domain. The existing perspiration detection algorithm proposed in the literature captures perspiration information by processing ridge lines in the time (spatial) domain. However, for some kinds of fingers (e.g., dry and perspiration-saturated fingers), changes in perspiration are minute. These changes are difficult to extract from the grey-level intensities processed in the time domain. Due to this, such fingers may be misclassified, thus reducing overall accuracy. In practice, we often encounter poor quality, dry or wet fingers. Therefore, it is necessary to take due care of such fingers, and have an enhanced algorithm that can process these fingers as well. To alleviate the problem, this paper discusses a new algorithm that processes time-series ridge lines using the multiresolution theory of wavelets. Major sweating changes are extracted at the coarse level, and then resolution is gradually increased to notice minute details. Such a coarse-to-fine strategy provides us with rich sweating information compared to that obtained directly from grey-level intensities in the time domain, which naturally leads to improved liveness results.