An Automatic Facial Localization Tracking Identification Biometric System through PCA and Wavelet Distribution in 3D Mesh Environment

Background/Objectives: In this paper, the process of automatically facial region localization and tracking in video frames through 3D mesh model is estimated. Methods/Statistical Analysis: The morphological regions of the face are modeled into 18 geometry based regions based on the various shape and expressions. The feature is estimated based on the covariance matrix in high region space. Then, it undergoes to PCA to estimate facial deformations. Findings: The patterns feature is extracted and it mapped with the multi geometry mapping. Then, the corresponding wavelet transforms extracted region into various dimension for geometry matching for classification. The proposed method achieves robustness based on its accurate transformation through wavelet analysis. The XM2VTSDB multi-modal face database is used to compare the real video sequence images. Application/Improvements: The experimental evaluation shows favorable results and yields 99% detection rate over 15000 video frame images. The frames in the facial tracking data are calculated through various parameters and compared with the ground truth against standard Euler angles of the muti-modal database.