As depth information is included, 3D palmprints are more competitive in anticounterfeiting. This chapter presents a novel person recognition method using 3D palmprint data. The full-field sinusoidal fringe projection technique is employed to collect 3D palmprint data remotely and quickly, from which the orientation feature of the mean curvature image is extracted through a revised Gabor filter. An effective feature matching strategy called the binary code list is proposed for classification. Using the developed capture system, a 3D palmprint database is established, and verification and identification experiments are performed. The PolyU 3D palmprint database is also used to evaluate the performance of the proposed recognition method. Compared with traditional single-mode feature-based 3D palmprint recognition methods, the proposed method is more accurate, efficient, and faster.