Generation of Facial Expressions through Manipulating Facial Parameters
Extracting discriminative features from salient facial patches plays an important role in effective facial expression recognition. The accurate detection of facial landmark needed to improve the localization of the salient patches on face images. This paper proposes framework for expression recognition by using appearance features of facial patches. By synthesize of emotional facial expressions through manipulating facial parameters, we aim to introduce facial emotion to a neutral facial image. For this, we perform a preprocessing process in which the face region is identified based on the skin color segmentation method. Then face is located with the connected area identification process. The feature points in facial region isexamined, extracted and get stored. In this algorithm bilinear transformation is used with in the mesh warping technique to manipulate the facial parameters. Thus we could generate some standard set of expressions like happy, angry, sad, fear, surprise.However, the optimal implementation of the proposed framework will significantly improve the computational cost and real-time expression recognition can be achieved with substantial accuracy. Further analysis and efforts are required to improve the performance by addressing some issues.
Cite this Article
Navami VS, Sindhiya. MP. Generation of Facial Expressions through Manipulating Facial Parameters. Journal of Artificial Intelligence Research & Advances. 2016; 3(2): 33–38p.
Abdesselam Bouzerdoum, Son Lam Phung, Fok Hing Chi Tivive, et al. Feature Selection for Facial Expression Recognition. 2010.
Adin Ramirez Rivera, Rojas Castillo Jorge A, Oksam Chae. Robust Facial Recognition based on Local Gaussian Structural Pattern. 2012. ISSN 1349-419.
Hsu C-W, Lin C-J. A Comparison of Methods for Multiclass Support Vector Machines. IEEE Trans. Mar 2002; 415–425p.
Kirchberg K, Jesorsky O, Frischholz R. Robust Face Detection using the Hausdorff Distance. In Proc 3rd Int Conf Audio Video-Based Biometric Person Authentication. 2001; 90–95p.
Zhao G, Pietikainen M. Dynamic Texture Recognition using Local Binary Patterns with an Application to Facial Expressions.
IEEE Trans Pattern Anal Mach Intell. Jun 2007; 29(6): 915–928p.
Zahid Ishraque SM, Taskeed Jabid, Oksam Chae. Face Recognition Based on Local Directional Pattern Variance (LDPv). Computer Engineering Department, Kyung Hee University, Republic of Korea.
Pantic M, Patras I. Dynamics of Facial Expression: Recognition of Facial Actions and their Temporal Segments from Face Profile Image Sequences. IEEE Trans Syst Man Cybern. Apr 2006; 36(2): 433–449p.
Khandait SP, Khandait SP, Khandait PD. Automatic Facial Feature Extraction and Expression Recognition based on Neural Network. International Journal of Advanced Computer Science and Applications (IJACSA). Jan 2011; 2(1).
Sokolova M, Lapalme G. A Systematic Analysis of Performance Measures for Classification Tasks. Inform Process Manage. 2009; 45(4): 427–437p.
Maja Pantic, Rothkrantz Leon JM. Facial Action Recognition for Facial Expression Analysis from Static Face Images. Jun 2004.
Michael Lyons, Shigeru Akamatsu. Coding Facial Expressions with Gabor Wavelets. Proceedings, Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara Japan, IEEE Computer Society. Apr 14–16 1998; 200–205p.
Ya Chang, Changbo Hu, Rogerio Feris, et al. Manifold Based Analysis of Facial Expression. Computer Science Department, University of California, Santa Barbara, CA 93106, USA Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA. Received 14 Dec 2004; received in revised form 30 Jun 2005; accepted 23 Aug 2005.
- There are currently no refbacks.