© 2014 Elsevier Ltd. All rights reserved. In this paper, we present a fully automated multimodal Curvelet-based approach for textured 3D face recognition. The proposed approach relies on a novel multimodal keypoint detector capable of repeatably identifying keypoints on textured 3D face surfaces. Unique local surface descriptors are then constructed around each detected keypoint by integrating Curvelet elements of different orientations, resulting in highly descriptive rotation invariant features. Unlike previously reported Curvelet-based face recognition algorithms which extract global features from textured faces only, our algorithm extracts both texture and 3D local features. In addition, this is achieved across a number of frequency bands to achieve robust and accurate recognition under varying illumination conditions and facial expressions. The proposed algorithm was evaluated using three well-known and challenging datasets, namely FRGC v2, BU-3DFE and Bosphorus datasets. Reported results show superior performance compared to prior art, with 99.2%, 95.1% and 91% verification rates at 0.001 FAR for FRGC v2, BU-3DFE and Bosphorus datasets, respectively.