Robust Posture Estimation of the Human Face in Rapid Lighting Changes using a 3-D Reference Picture

Recent studies of biometrics related with recognition of human postures and specific domain features for personal authentications by employing 3-dimensional (3D) face-profiling models have triggered much attention. However, changes in posture and lighting remain as critical issues in face-profiling to date. The human face has been and is still the biometrics most extensively exploited in daily-life personal authentications. Factors of 3D perspectives include color and brightness, which vary with the direction and intensity of illuminations, are of critical influence with difficult practical resolution. In addition, changes in the face domain may be abrupt, instantaneous and unpredictable, thus complicating image detection and extraction to eventually reduce precision in graphic recognition. In the present study, we innovated an economically viable and technically reliable technique to accurately estimate the face-posture under sudden changes in illumination. Our approach involves prior storage of 3D reference images with characteristic extraction of subjects for subsequent posture estimation of 2D movie images. Posture estimation-processing of circumstantial changes and posture predicts movement using a vector that predicts and collars according to the texture status. Our investigations revealed that posture estimation is independent of illumination changes even at high speed when 3D reference images are rotated in sync with posture estimation-processing