Collecting 3D Point Cloud by Kinect Sensor with Low Resolution for Robust Face Recognition
Song Guo-pin · 2014
Traditional three dimensional face recognition algorithms can not deal with robust face recognition but with high costs, a robust face recognition system based on Kinect sensor with low resolution collecting 3D point cloud is designed. Firstly, standardized texture images are got by tip detecting, face cutting, posture correcting, symmetry filling and smooth sampling. Then, discriminant color space transform is used on texture images to maximize separabilities between classes. Finally, multi-modal sparse coding is used to reconstruct errors so as to getting similarities between the query image and total training set, and Z-scoring technique is used to recognize face. Recognition accuracy of proposed algorithm can achieve 96.7% on common face databases CurtinFaces, PIE and AR. Experimental results show that proposed algorithm has higher recognition accuracy and better recognition efficiency than several advanced face recognition algorithms.