The paper presents a filter-based algorithm for fingerprint verification that uses a bank of Gabor filters to capture both local and global details in a fingerprint, resulting in a compact fixed-length FingerCode. The matching process is based on the Euclidean distance between the FingerCodes, making it fast and efficient. The authors achieve verification accuracy comparable to state-of-the-art minutiae-based algorithms, with improved performance when the false acceptance rate (FAR) requirements are less stringent. The system outperforms a state-of-the-art minutiae-based system in certain scenarios. The paper also discusses the challenges and limitations of the proposed method, such as the difficulty in accurately locating the reference point in noisy images and the inability to handle large deformations in ridge patterns due to finger pressure differences. The primary advantage of the filter-based approach is its computational efficiency and its suitability for indexing and hardware implementation.The paper presents a filter-based algorithm for fingerprint verification that uses a bank of Gabor filters to capture both local and global details in a fingerprint, resulting in a compact fixed-length FingerCode. The matching process is based on the Euclidean distance between the FingerCodes, making it fast and efficient. The authors achieve verification accuracy comparable to state-of-the-art minutiae-based algorithms, with improved performance when the false acceptance rate (FAR) requirements are less stringent. The system outperforms a state-of-the-art minutiae-based system in certain scenarios. The paper also discusses the challenges and limitations of the proposed method, such as the difficulty in accurately locating the reference point in noisy images and the inability to handle large deformations in ridge patterns due to finger pressure differences. The primary advantage of the filter-based approach is its computational efficiency and its suitability for indexing and hardware implementation.