Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Face recognition with partial face images is an important problem in face biometrics. The necessity can arise in not so constrained environments such as in surveillance video, or portal video as provided in Multiple Biometrics Grand Challenge (MBGC). Face alignment with partial face images is a key step toward this challenging problem.#R##N##R##N#,  present a method for partial face alignment based on scale invariant feature transform (SIFT).  first train a reference model using holistic faces, in which the anchor points and their corresponding descriptor subspaces are learned from initial SIFT keypoints and the relationships beten the anchor points are also derived. In the alignment stage, correspondences beten the learned holistic face model and an input partial face image are established by matching keypoints of the partial face to the anchor points of the learned face model. Furthermore, shape constraint is used to eliminate outlier correspondences and temporal constraint is explored to find more inliers. Alignment is finally accomplished by solving a similarity transform. Experiments on the MBGC near infrared video sequences show the effectiveness of the proposed method, especially when PCA subspace, shape and temporal constraint are utilized.
['Jimei Yang', 'Shengcai Liao', 'Stan Z. Li']
I am writing a research paper on a idea Automatic Partial Face Alignment in NIR Video Sequences
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Partial Face Alignment in NIR Video Sequences"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Face recognition with partial face images is an important problem in face biometrics. The necessity can arise in not so constrained environments such as in surveillance video, or portal video as provided in Multiple Biometrics Grand Challenge (MBGC). Face alignment with partial face images is a key step toward this challenging problem.#R##N##R##N#,  present a method for partial face alignment based on scale invariant feature transform (SIFT).  first train a reference model using holistic faces, in which the anchor points and their corresponding descriptor subspaces are learned from initial SIFT keypoints and the relationships beten the anchor points are also derived. In the alignment stage, correspondences beten the learned holistic face model and an input partial face image are established by matching keypoints of the partial face to the anchor points of the learned face model. Furthermore, shape constraint is used to eliminate outlier correspondences and temporal constraint is explored to find more inliers. Alignment is finally accomplished by solving a similarity transform. Experiments on the MBGC near infrared video sequences show the effectiveness of the proposed method, especially when PCA subspace, shape and temporal constraint are utilized."}]