Robust 3D face recognition in presence of pose and partial occlusions or missing parts
This addresses robust face recognition for security or biometric applications, but it is incremental as it combines existing methods like ICP and PCA for occlusion handling.
The paper tackles 3D face recognition under pose variations and partial occlusions by developing a system that registers faces using ICP, detects occlusions via depth thresholding, restores them with PCA, and extracts face normal features for classification. It achieves 91.30% recognition accuracy on the Bosphorus 3D face database.
In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions in real world. The system at first takes as input, a 3D range image, simultaneously registers it using ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model by minimizing distances between a probe model and a gallery model. However the performance of ICP relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will be improved iteratively and finally converge to the best alignment possible. Once the faces are registered, the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored images, after the removal of occlusions, are then fed to the recognition system for classification purpose. Features are extracted from the reconstructed non-occluded face images in the form of face normals. The experimental results which were obtained on the occluded facial images from the Bosphorus 3D face database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of 91.30%.