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In this article, we propose a three-dimensional (3D) face recognition approach for depth data captured by Kinect based on a combination of speeded up robust features (SURF) and k-nearest neighbor (KNN) algorithms. First, the shape index maps of the preprocessed 3D faces of the training gallery are computed, then the SURF feature vectors are extracted and used to form the dictionary. In the recognition process, we propose an improved KNN classifier to find the best match. The evaluation was performed using CurtinFaces and KinectFaceDB data sets, achieving rank-1 recognition rates of 96.78% and 94.23%, respectively, when using two samples per person for training.

An Improved KNN Classifier for 3D Face Recognition Based on SURF Descriptors