Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

Minutiae-based methods have been used in many commercial fingerprint matching systems. Based primarily on a point pattern matching model, these methods rely heavily on the accuracy of minutiae extraction and the detection of landmarks like core and delta for pre-alignment. Spurious and missing minutiae can both introduce errors in minutiae correspondence. Equally problematic is the inability to detect landmarks to guide pre-alignment. Taken together, these problems lead to sub-optimal matching accuracy. Fortunately, the contextual information provided by ridge flow and orientation in the neighborhood of detected minutiae can help eliminate spurious minutiae while compensating for the absence of genuinely missing minutiae both before and during matching. In addition, coupled with a core detection algorithm that can robustly handle missing or partially available landmarks for pre-alignment, significant improvement in matching accuracy can be expected. In this chapter, we will firstly review fingerprint feature extraction, minutiae representation, and registration, which are important components of fingerprint matching algorithms. Following this, we will detail a relevant fingerprint matching algorithm based on the Shape Context descriptor found in Kwan et al. (2006). Next, we will introduce a novel hybrid shape and orientation descriptor that is designed to address the above problems. The hybrid descriptor can effectively filter out spurious or unnatural minutiae pairings while simultaneously using the additional ridge orientation cues in improving match score calculation. In addition, the proposed method can handle situations where either the cores are not well defined for detection or the fingerprints have only partial overlapping. Lastly, experiments conducted on two publicly available fingerprint databases confirm that the proposed hybrid method outperforms other methods included in our performance comparison.