Abstract-We utilize ideas from two growing but disparate ideas in computer vision-shape analysis using tools from differential geometry and feature selection using machine learning-to select and highlight salient geometrical facial features that contribute most in 3-D face recognition and gender classification. First, a large set of geometries curve features are extracted using level sets (circular curves) and streamlines (radial curves) of the Euclidean distance functions of the facial surface; together they approximate facial surfaces with arbitrarily high accuracy. Then, we use the wellknown Adaboost algorithm for feature selection from this large set and derive a composite classifier that achieves high performance with a minimal set of features. This greatly reduced set, consisting of some level curves on the nose and some radial curves in the forehead and cheeks regions, provides a very compact signature of a 3-D face and a fast classification algorithm for face recognition and gender selection. It is also efficient in terms of data storage and transmission costs. Experimental results, carried out using the FRGCv2 dataset, yield a rank-1 face recognition rate of 98% and a gender classification rate of 86% rate.
For example, Ballihi et al. REF used a large set of geometric-curve features (circular curves and radial curves) together with the Adaboost algorithm for feature selection to yield a gender classification rate of 86% on the FRGCv2 dataset.
Boosting 3-D-Geometric Features for Efficient Face Recognition and Gender Classification
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security
Computer Science