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Facial recognition is the most natural and common form of biometrics, routinely used by humans and one of the most promising areas in biometrics research. The majority of traditional researches and commercial use of facial recognition systems are focused on methods that explores 2D (two-dimensional) images of human faces. All of them are based on features extraction that does not use any 3D shape information from the faces, especially with regard to depth. This paper presents a method based on Point Cloud and Curvature Map Projection to perform a 3D face recognition. The achieved results are presented and divided in two test scenarios, composed by a biometric evaluation analysis applying the Equal Error Rate score, Receiver Operating Characteristic and an accuracy comparison with other related works. The proposed work presents an accuracy of about 98.92%, allowing it to be applied for 3D face recognition tasks.

3D Face Recognition on Point Cloud Data - An Approaching based on Curvature Map Projection using Low Resolution Devices