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

3D Face Recognition in the Presence of Partial Data

2014 ◽  
pp. 70-97
Author(s):  
Dat Chu ◽  
Shishir Shah ◽  
Ioannis A. Kakadiaris
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Recognition Task ◽  
Training Data ◽  
3D Face Recognition ◽  
Coupling Term ◽  
3D Face ◽  
The Face ◽  
The Right

Performing face recognition under extreme poses and lighting conditions remains a challenging task for current state-of-the-art biometric algorithms. The recognition task is even more challenging when there is insufficient training data available in the gallery, or when the gallery dataset originates from one side of the face while the probe dataset originates from the other. The authors present a new method for computing the distance between two biometric signatures acquired under such challenging conditions. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost, and the semi-coupling cost. The use of a semi-coupling term allows the method to handle partial 3D face meshes where, for example, only the left side of the face is available for gallery and the right side of the face is available for probe. The method also extends to 2D signatures under varying poses and lighting changes by using 3D signatures as a coupling term. The experiments show that this method can improve recognition performance of existing state-of-the-art wavelet signatures used in 3D face recognition and provide excellent recognition results in the 3D-2D face recognition application.

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

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.

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