title: A general framework for face reconstruction using single still image based on 2D-to-3D transformation kernel.
abstract: Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression.
authors: Faculty of Information Science and Technology, Mahanakorn University and Technology, 140 Cheum-sampan Rd., Nongchok, Bangkok 10530, Thailand. Electronic address: f.rerkchai@gmail.com.
authors: Rerkchai
authors: R
authors: Fooprateepsiri
authors: Faculty of Information Science and Technology, Mahanakorn University and Technology, 140 Cheum-sampan Rd., Nongchok, Bangkok 10530, Thailand. Electronic address: werasak@mut.ac.th.
authors: Werasak
authors: W
authors: Kurutach
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D003198:Computer Simulation; D019992:Databases as Topic; D005145:Face / Q000033:anatomy & histology*; D005149:Facial Expression; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008029:Lighting; D008954:Models, Biological; D012880:Skin Pigmentation
keywords: Face detection; Face recognition; Face reconstruction; Gradient vector flow; View synthesis