Abstract
This paper proposes a feature-based face recognition system based on both 3D range data as well as 2D gray-level facial images. Feature points are designed to be robust against changes of facial expressions and viewpoints and are described by Gabor Wavelet filter in the 2D domain and Point Signature in the 3D domain. Localizing feature points in a new image is based on 3D-2D correspondence, their relative position and corresponding Bunch (covering a wide range of possible variations on each feature point). Extracted shape and texture features from these feature points are first projected into their own eigenspace using PCA. In eigenspace, the corresponding shape and texture weight vectors are further integrated through a normalization procedure to form an augmented vector which is used to represent each facial image. For a given test facial image, the best match in the model library is identified according to a similarity function. Experimental results involving 20 persons with different facial expressions and extracted from different view points have demonstrated the efficiency of our algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.