Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics.
['Erhan A. Ince', 'Syed Amjad Ali']
I am writing a research paper on a idea Rule Based Segmentation and Subject Identification Using Fiducial Features and Subspace Projection Methods
[{"from": "human", "value": "I am writing a research paper on a idea Rule Based Segmentation and Subject Identification Using Fiducial Features and Subspace Projection Methods"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a framework for carrying out face recognition on a subset of standard color FERET database using two different subspace projection methods, namely PCA and Fisherfaces. At first, a rule based skin region segmentation algorithm is discussed and then details about eye localization and geometric normalization are given. The work achieves scale and rotation invariance by fixing the inter ocular distance to a selected value and by setting the direction of the eye-to-eye axis. Furthermore, the work also tries to avoid the small sample space (S3) problem by increasing the number of shots per subject through the use of one duplicate set per subject. Finally, performance analysis for the normalized global faces, the individual extracted features and for a multiple component combination are provided using a nearest neighbour classifier with Euclidean and/or Cosine distance metrics."}]