Multi Modal Face Recognition Using Block Based Curvelet Features
In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.
OBJECTIVE ::: To establish a new method to discriminate Aaron's beard by Fourier transformation infrared spectrometry. ::: ::: ::: METHODS ::: Attenuation Reflection-Fourier transformation infrared spectrometry with clustering analysis was used to the identification of Aaron's beard. ::: ::: ::: RESULTS ::: There were obvious differences in Aaron's beard. The results are consistent with that of morphologic study. ::: ::: ::: CONCLUSION ::: This method is rapid, simple and economical, and can be used to the quality control.