Matching 2.5D face scans to 3D models

By Xiaoguang Lu, Anil K. Jain, Dirk Colbry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

376 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression (2006, methodology): “For the baseline algorithms, we use a PCA approach similar to previous work [1], [13], [14] and an iterative closest point (ICP) approach similar to previous work [2], [10], [15].”
F Mean Squared Error: Love It or Leave It? (2009, methodology): “False Acceptance Rate 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 CW-SSIM MSEZ MSECP HAUSDORFF F al se R ej ec tio n R at e (b)(a) IEEE SIGNAL PROCESSING MAGAZINE [114] JANUARY 2009 THERE IS LITTLE DOUBT THAT IF ALL OF THE FUNCTIONAL COMPONENTS OF A HUMAN PERCEPTUAL SYSTEM WERE PRECISELY SIMULATED, THEN”
Complex Wavelet Structural Similarity: A New Image Similarity Index (2009, methodology): “of a human face is represented as a point cloud and two facial surfaces are compared using the geometry-based indices of [49]–[51], [52] or PDHM [53]–[56].”