Face Recognition Method Combining 3D Face Model with 2D Recognition

One of the main problems in Face Recognition systems is the recognition of an input face with a different expression than the available in the training database. In this work, we propose a new 3D‐face expression synthesis approach for expression independent face recognition systems (FRS). Different than current schemes in the literature, all the steps involved in our approach (face denoising, registration, and expression synthesis) are performed in the 3D domain. Our final goal is to increase the flexibility of 3D‐FRS by allowing them to artificially generate multiple face expressions from a neutral expression face. A generic 3D‐range image is modeled by the Finite Element Method with three simplified layers representing the skin, fatty tissue and the cranium. The face muscular anatomy is superimposed to the 3D model for the synthesis of expressions. Our approach can be divided into three main steps: Denoising Algorithm, which is applied to remove long peaks present in the original 3Dface samples; Automatic Control Points Detection, to detect particular facial landmarks such as eye and mouth corners, nose tip, etc., helpful in the recognition process; Face Registration of a 3D‐face model with each sample face with neutral expression in the training database in order to augment its training set (with 18 predefined expressions). Additional expressions can be learned from input faces or an unknown expression can be transformed to the closest known expression. Our results show that the 3D‐face model resembles perfectly the neutral expression faces in the training database while providing a natural change of expression. Moreover, the inclusion of our expression synthesis approach in a simple 3D‐FRS based on Fisherfaces increased significantly the recognition rate without requiring complex 3D‐face recognition chemes.

Download Full-text

Research on an Improved MB-LBP 3D Face Recognition Method

Journal of Software ◽

10.17706/jsw.16.6.306-314 ◽

2021 ◽

pp. 306-314

Author(s):

Liangliang Shi ◽

◽

Xia Wang ◽

Yongliang Shen

Keyword(s):

Face Recognition ◽

Recognition Rate ◽

Depth Image ◽

Support Vector ◽

3D Face Recognition ◽

Recognition Method ◽

Recognition Time ◽

3D Face ◽

Feature Information ◽

Average Information

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.

Download Full-text

3D face recognition method against gesture and expression change

Journal of Applied Optics ◽

10.5768/jao201839.0402002 ◽

2018 ◽

Vol 39 (4) ◽

pp. 41-45 ◽

Cited By ~ 1

Author(s):

Cai Chuanli ◽

Zhang Jianping ◽

Zhang Yanbo

Keyword(s):

Face Recognition ◽

3D Face Recognition ◽

Recognition Method ◽

Expression Change ◽

3D Face

Download Full-text

Expression invariant face recognition using multi-stage 3D face fitting with 3D morphable face model

2010 International Conference on Computer Applications and Industrial Electronics ◽

10.1109/iccaie.2010.5735065 ◽

2010 ◽

Cited By ~ 1

Author(s):

Abdallah A. Alomari ◽

Fatimah Khalid ◽

Rahmita Wirza O. K. Rahmat ◽

Muhammad Taufik Abdallah

Keyword(s):

Face Recognition ◽

Face Model ◽

3D Face ◽

Multi Stage

Download Full-text