Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A facial feature extraction algorithm using the Bayesian shape model (BSM) is proposed in this paper. A full-face model consisting of the contour points and the control points is designed to describe the face patch, using which the warping/normalization of the extracted face patch can be performed e5ciently. First, the BSM is utilized to match and extract the contour points of a face. In BSM, the prototype of the face contour can be adjusted adaptively according to its prior distribution. Moreover, an a5ne invariant internal energy term is introduced to describe the local shape deformations beten the prototype contour in the shape domain and the deformable contour in the image domain. Thus, both global and local shape deformations can be tolerated. Then, the control points are estimated from the matching result of the contour points based on the statistics of the full-face model. Finally, the face patch is extracted and normalized using the piece-wise a5ne triangle warping algorithm. Experimental results based on real facial feature extraction demonstrate that the proposed BSM facial feature extraction algorithm is more accurate and e8ective as compared to that of the active shape model (ASM). ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
['Zhong Xue', 'Stan Z. Li', 'Eam Khwang Teoh']
I am writing a research paper on a idea Bayesian shape model for facial feature extraction and recognition
[{"from": "human", "value": "I am writing a research paper on a idea Bayesian shape model for facial feature extraction and recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A facial feature extraction algorithm using the Bayesian shape model (BSM) is proposed in this paper. A full-face model consisting of the contour points and the control points is designed to describe the face patch, using which the warping/normalization of the extracted face patch can be performed e5ciently. First, the BSM is utilized to match and extract the contour points of a face. In BSM, the prototype of the face contour can be adjusted adaptively according to its prior distribution. Moreover, an a5ne invariant internal energy term is introduced to describe the local shape deformations beten the prototype contour in the shape domain and the deformable contour in the image domain. Thus, both global and local shape deformations can be tolerated. Then, the control points are estimated from the matching result of the contour points based on the statistics of the full-face model. Finally, the face patch is extracted and normalized using the piece-wise a5ne triangle warping algorithm. Experimental results based on real facial feature extraction demonstrate that the proposed BSM facial feature extraction algorithm is more accurate and e8ective as compared to that of the active shape model (ASM). ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved."}]