Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition
This work addresses biometric recognition challenges, offering a scalable solution for high-dimensional data, though it appears incremental as it builds on existing quadratic matrix learning methods.
The paper tackles the problem of feature extraction for biometric recognition by proposing a novel quadratic projection framework, achieving superior performance compared to state-of-the-art algorithms on face, palmprint, and ear recognition tasks.
This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed 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 of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithms