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This paper presents a novel quadratic projection based feature extractionframework, where a set of quadratic matrices is learned to distinguish eachclass from all other classes. We formulate quadratic matrix learning (QML) as astandard semidefinite programming (SDP) problem. However, the con- ventionalinterior-point SDP solvers do not scale well to the problem of QML forhigh-dimensional data. To solve the scalability of QML, we develop an efficientalgorithm, termed DualQML, based on the Lagrange duality theory, to extractnonlinear features. To evaluate the feasibility and effectiveness of theproposed framework, we conduct extensive experiments on biometric recognition.Experimental results on three representative biometric recogni- tion tasks,including face, palmprint, and ear recognition, demonstrate the superiority ofthe DualQML-based feature extraction algorithm compared to the currentstate-of-the-art algorithms

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition