We propose a unified unimodal biometric system that is suitable for most individual modalities, e.g., face and gait. The proposed system consists of three steps: (1) preprocessing raw biometric data, (2) determining the intrinsic low-dimensional subspace of preprocessed data by local topology structure preserving projections (LTSPP), and (3) performing the classification in the determined subspace using the intraclass distance sum. In the proposed system, LTSPP is a novel subspace algorithm that focuses not only on the class information but also on the local topology structure. In terms of representing the separability of different classes, LTSPP projects the interclass margin data far apart. Meanwhile, LTSPP preserves the intraclass topology structures by using linear reconstruction coefficients. Compared with other subspace methods, LTSPP possesses more discriminant abilities and is more suitable for biometric recognition. In addition, both preprocessing each raw datum into unit and performing the classification using the intraclass distance sum are helpful to improve the recognition rates. We carry out various recognition experiments using the Yale and HumanID gait databases. The encouraging experimental results demonstrate the effectiveness of our unified unimodal biometric system, and the proposed LTSPP algorithm for this system can yield the best recognition rates compared to the other algorithms.