scholarly journals 3D Face Recognition Using Anthropometric and Curvelet Features Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Dan Song ◽  
Jing Luo ◽  
Chunyuan Zi ◽  
Huixin Tian
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Multiple Scales ◽  
Curvelet Transform ◽  
Facial Feature ◽  
Recognition Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Features Fusion ◽  
Anthropometric Characteristics

Curvelet transform can describe the signal by multiple scales, and multiple directions. In order to improve the performance of 3D face recognition algorithm, we proposed an Anthropometric and Curvelet features fusion-based algorithm for 3D face recognition (Anthropometric Curvelet Fusion Face Recognition, ACFFR). First, the eyes, nose, and mouth feature regions are extracted by the Anthropometric characteristics and curvature features of the human face. Second, Curvelet energy features of the facial feature regions at different scales and different directions are extracted by Curvelet transform. At last, Euclidean distance is used as the similarity between template and objectives. To verify the performance, the proposed algorithm is compared with Anthroface3D and Curveletface3D on the Texas 3D FR database. The experimental results have shown that the proposed algorithm performs well, with equal error rate of 1.75% and accuracy of 97.0%. The algorithm we proposed in this paper has better robustness to expression and light changes than Anthroface3D and Curveletface3D.

Application of Gabor Dictionary and SL0 Norm in Disguise Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1211-1214
Author(s):  
Jun Ying Zeng ◽  
Jun Ying Gan ◽  
Yi Kui Zhai
Keyword(s):  
Face Recognition ◽  
Function Approximation ◽  
Multiple Scales ◽  
Recognition Rate ◽  
Principal Component ◽  
Facial Feature ◽  
Training Image ◽  
Recognition Algorithm ◽  
The Face ◽  
Gabor Dictionary

A fast sparse representation face recognition algorithm based on Gabor dictionary and SL0 norm is proposed in this paper. The Gabor filters, which could effectively extract local directional features of the image at multiple scales, are less sensitive to variations of illumination, expression and camouflage. SL0 algorithm, with the advantages of calculation speed，require fewer measurement values by continuously differentiable function approximation L0 norm and reconstructed sparse signal by minimizing the approximate L0 norm. The algorithm obtain the local feature face by extracting the Gabor face feature, reduce the dimensions by principal component analysis, fast sparse classify by the SL0 norm. Under camouflage condition, The algorithm block the Gabor facial feature and improve the speed of formation of the Gabor dictionary. The experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate to some extent and can generalize well to the face recognition, even with a few training image per class.

3D Face Recognition Under Isometric Expression Deformations

2014 ◽  
Author(s):  
Javad Sovizi ◽  
Rahul Rai ◽  
Venkat Krovi
Keyword(s):  
Face Recognition ◽  
Geodesic Distance ◽  
Recognition Task ◽  
Shape Descriptor ◽  
Recognition Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Topological Features ◽  
Isometric Deformation ◽  
Isometric Deformations

In this paper, 3D face recognition under isometric deformation (induced by facial expressions) is considered. The main objective is to employ the shape descriptors that are invariant to (isometric) deformations to provide an efficient face recognition algorithm. Two methods of the correspondence are utilized for automatic landmark assignment to the query face. One is based on the conventional iterative closest point (ICP) method and another is based upon the geometrical/topological features of the human face. The shape descriptor is chosen to be the well-known geodesic distance (GD) measure. The recognition task is performed on SHREC08 database for both correspondence methods and the effect of feature (GD) vector size as well as landmark positions on the recognition accuracy were argued.

An Efficient 3D Face Recognition Algorithm

2009 ◽  
Author(s):  
Vandana D. Kaushik ◽  
Aditya Budhwar ◽  
Anuj Dubey ◽  
Rahul Agrawal ◽  
Shraddha Gupta ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
3D Face Recognition ◽  
3D Face

The 3D Face Recognition Algorithm Fusing Multi-geometry Features

2009 ◽  
Vol 34 (12) ◽  
pp. 1483-1489 ◽  
Author(s):  
Yan-Feng SUN ◽  
Heng-Liang TANG ◽  
Bao-Cai YIN
Keyword(s):  
Face Recognition ◽  
Recognition Algorithm ◽  
3D Face Recognition ◽  
3D Face
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