In this thesis, we focus on the fingerprint, which is the most widely used in the biometric community. The main contribution of this thesis is as follows: We develop an improvement version of fuzzy vault integrating minutiae’s local ridge orientation information. The improved fuzzy fingerprint vault, a two factor authentication scheme to some extent, can effectively prevent cross-matching between different fingerprint vaults. Experimental results show that, if and only if the fingerprint and the password of users are simultaneity obtained by the attacker, the fuzzy vault can be cracked. An alignment-free fingerprint cryptosystem based on fuzzy vault scheme is developed fusing the local features, known minutia descriptor and minutia local structure, which are invariant to the transformation in fingerprint capturing. Three fusion strategies are employed to integrate the two local features. Huffman coding technology is used to compress the storage volume of the minutia descriptor vault. The proposed fingerprint cryptosystem can avoid the alignment procedure and improve the performance and security of the fuzzy vault scheme at the same time. A novel fingerprint aligning method is proposed, which integrates the fingerprint reference points and its neighboring region of interest(ROI) in a hierarchical manner. The concept of mutual information(MI) in the information theory is used to assess the coincidence extent of two fingerprints after being aligned. The novel alignment method is applied to fingerprint-based fuzzy vault implementation. Out of information leakage consideration, the orientation features of fingerprint minutiae are discarded and another distinguishing local feature, inter-minutiae ridge count, is used to replace the minutiae orientation in the implementation of fingerprint-based fuzzy vault. We propose a novel binary length-fixed feature generation method of fingerprint. The alignment procedure, which is thought as a difficult task in the encrypted domain, is avoided in the proposed method due to the employment of minutiae triplets (MT). Using the generated binary feature as input, we construct the biometric cryptosystems by combining several of error correction codes, including BCH code, a concatenated code of BCH code and Reed-Solomon code, and LDPC code, based on fuzzy commitment scheme.