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In terms of biometrics, a human finger itself is with trimodal traits including fingerprint, finger-vein, and finger-knuckle-print, which provides convenience and practicality for finger trimodal fusion recognition. The scale inconsistency of finger trimodal images is an important reason affecting effective fusion. It is therefore very important to developing a theory of giving a unified expression of finger trimodal features. In this paper, a graph-based feature extraction method for finger biometric images is proposed. The feature expression based on graph structure can well solve the problem of feature space mismatch for the finger three modalities. We provide two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion. The research results can not only promote the advancement of finger multimodal biometrics technology but also provide a scientific solution framework for other multimodal feature fusion problems. The experimental results show that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.