The paper presents a new filter bank-based fingerprint feature extraction and matching method without needing to detect minutiae. The proposed method decomposes a fingerprint image into eight directional subband outputs using a directional filter bank (DFB) and then obtains directional energy distributions for each block from the decomposed subband outputs. Only dominant directional energy components are employed as elements of the input feature vector, which serves to reduce noise and improve efficiency. For the rotational alignment, additional input feature vectors in which various rotations are considered are extracted, and these input feature vectors are compared with the enrolled template feature vector. The proposed method significantly reduces the memory cost and processing time associated with verification, primarily because of the efficient DFB structure and the exploitation of directional specific information. Experimental results validate the effectiveness of the proposed method in extracting fingerprint features and achieving good performance.