3D Face Recognition Based on Local Curvature Feature Matching

2011 ◽  
Vol 121-126 ◽  
pp. 609-616
Author(s):  
Dao Qing Sheng ◽  
Guo Yue Chen ◽  
Kazuki Saruta ◽  
Yuki Terata
Keyword(s):  
Face Recognition ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
High Performance ◽  
Feature Matching ◽  
3D Face Recognition ◽  
3D Point Cloud ◽  
Local Curvature ◽  
3D Face ◽  
B Splines

In this paper, an approach based on local curvature feature matching for 3D face recognition is proposed. K-L transformation is employed to adjust coordinate system and coarsely align 3D point cloud. Based on B-splines approximation, 3D facial surface reconstruction is implemented. Through analyzing curvature features of the fitted surface, local rigid facial patches are extracted. According to the extracted local patches, feature vectors are constructed to execute final recognition. Experimental results demonstrate high performance of the presented method and also show that the method is fairly effective for 3D face recognition.

Face Recognition Algorithm Based on 3D Point Cloud Acquired by Mixed Image Sensor

2019 ◽  
Vol 23 (2) ◽  
pp. 366-369
Author(s):  
Tang-Tang Yi ◽  
Keyword(s):  
Face Recognition ◽  
Point Cloud ◽  
Recognition Accuracy ◽  
Image Sensor ◽  
Recognition Algorithm ◽  
Face Image ◽  
3D Point Cloud ◽  
3D Face ◽  
Face Images ◽  
The Face

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.

scholarly journals 3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

2019 ◽  
Vol 19 (2) ◽  
pp. 28-37
Author(s):  
Hawraa H. Abbas ◽  
Bilal Z. Ahmed ◽  
Ahmed Kamil Abbas
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
3D Face Recognition ◽  
Shape Variation ◽  
3D Face ◽  
The Face

Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

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