Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent pose invariant methods try to model the subject specific appearance change across pose. For this, hover, almost all of the existing methods require a perfect alignment beten a gallery and a probe image.   present a pose invariant face recognition method, centered on modeling joint appearance of gallery and probe images across pose, that do not require the facial landmarks to be detected as such.  propose novel extensions by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features  put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment beten gallery and probe images which makes it particularly attractive as compared to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions.
['M. Sarfraz .', 'Olaf Hellwich']
I am writing a research paper on a idea Statistical appearance models for automatic pose invariant face recognition
[{"from": "human", "value": "I am writing a research paper on a idea Statistical appearance models for automatic pose invariant face recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recent pose invariant methods try to model the subject specific appearance change across pose. For this, hover, almost all of the existing methods require a perfect alignment beten a gallery and a probe image.   present a pose invariant face recognition method, centered on modeling joint appearance of gallery and probe images across pose, that do not require the facial landmarks to be detected as such.  propose novel extensions by introducing to use a more robust feature description as opposed to pixel-based appearances. Using such features  put forward to synthesize the non-frontal views to frontal. Furthermore, using local kernel density estimation, instead of commonly used normal density assumption, is suggested to derive the prior models. Our method does not require any strict alignment beten gallery and probe images which makes it particularly attractive as compared to the existing state of the art methods. Improved recognition across a wide range of poses has been achieved using these extensions."}]