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Fingerprint match-on-card is receiving more attention from society because of higher level of security and less privacy concern. The ISO/IEC19794-2 for match-on-card defines the minutiae data format that contains the basic information. Therefore, match-on-card is a challenge, especially when dealing with deformation and common correspondence for alignment. In this paper, a novel in-matcher clustering method is proposed to search for the matched minutia clusters to cater for deformation. Moreover, a further matching step using Mahalanobis distance to measure the inter-cluster similarity is proposed to remove the wrongly matched clusters. Finally, the overall match score is generated by combining the scores from matched clusters and the geometrical structure of clusters. The proposed algorithm achieved an average EER ⇐ 5.1979% using all FVC databases. In the NIST evaluation, the achieved false match rate (FMR) = 0.001 and false non-match rate (FNMR) = 0.08 and the average on-card verification time is 1.01s using 8-bit smartcard.