Generalized PCA Face Recognition by Image Correction and Bit Feature Fusion
In this paper, two approaches to improve the illumination robustness of the face recognition algorithms are presented, that is, Symmetrical Image Correction (SIC) and Bit-Plan Feature Fusion (BPFF). SIC can reduce bright speckles and shadows caused by over lighting. BPFF constructs a new virtual face with Bit-Plan information of face images. Generalized PCA is then applied to the virtual faces to achieve face recognition. Experiments show that, the proposed combined method can reduce the sensitivity of face recognition to illuminations using fewer projection vectors than the compared approaches.
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.