Comparison and usage of local feature based algorithms for 3D face recognition

With the development of laser scanning technology, 3D point clouds have become easy to obtain. Thus, facial recognition has become a popular field of study by using a three-dimensional point cloud against the constraints of automatic face recognition using two-dimensional images. The aim of the study is to approach 3D face recognition processes from a different dimension. In this context, the facilities of using automatic 3D local keypoint detector algorithms in face recognition are being investigated. In the scope of the thesis, face recognition algorithm was developed using 3D keypoint based methods. As an application data, face data belonging to 10 people were modeled in 3D by using a laser scanner. The algorithm consists of three steps. In the first step, 3D points are defined on the point clouds using Instrinsic Shape Signature (ISS) method. In the second step, key points are defined using Point Feature Histograms (PFH) and Fast Point Feature Histograms (FPFH) histogram methods. In the third step, the keypoints in different point clouds are matched using the feature histograms obtained. As a results, in the natural face expression, ISS-PFH algorithm, 9 out of 10 people; 7 out of 10 people with ISS-FPFH algorithm are correctly defined. When the cases where different face expressions are given to the system are examined, the ISS-PFH algorithm has 5 out of 10 persons; The ISS-FPFH algorithm has 3 out of 10 people correctly identified. The positional accuracy of the matched points has been examined. ICP was applied to the matching point clouds for this purpose. Euclidean distance between corresponding keypoints in the two point cloud is calculated. It has been accepted that the points are shorter than 10 mm. When root mean square errors of correct point matches are examined, there is no significant difference between the methods. In all methods a root mean square error of about 3 mm was determined with an accuracy of 10 mm. The difference between keypoint descriptor algorithms has been determined. The correct matching rate for PFH is up to 60\% with 10 mm error, while FPFH histograms are around 25\% - 30\%.