An evaluation of denoising algorithms for 3D face recognition

Although 3D face imaging is increasingly popular, many 3D facial imaging systems have significant noise components which needs to be reduced by post-processing if meaningful recognition results are desired. For best results, the denoising algorithm must be chosen appropriately, using the noise distribution, and its parameters tuned. In this paper, a thorough analysis on the influence of different denoising techniques on the performance of various holistic 3D face recognition techniques is provided. After resampling and denoising the input data, the face is cropped, aligned and normalised. This results in the feature space which is split into two sets: gallery and query. Recognition ranks are then computed on the feature space for different denoising algorithms. Result show that, despite its simplicity, the median filter produces the best results. However, its best results are achieved using a mask size much larger than is commonly used in 3D facial denoising. For all techniques, results show that denoising can be applied using parameters significantly higher than those traditionally used as this improves the recognition performance.