Application of Robust Spectral Regressn Improved by APG in Face Recognition
Huang Mia · Science Technology and Engineering · 2014
For the problem that traditional spectral regression approaches are sensitive to the errors incurred by class label error and occlusion in face recognition,robust spectral regression based on accelerated proximal gradient( APG-RSR) is proposed. Firstly,the robust discriminant subspace learning problem is formulated as a maximum conrrentropy problem,which can help to find the most correlation solution between spectral targets and predictions.Then,total variation( TV) regularization is imposed on the conrrentropy objective to learn a spatially smooth face structure. Finally,the maximum conrrentropy problem is casted into a compound regularization model based on the additive form of half-quadratic optimization,which can be efficiently optimized via an accelerated proximal gradient algorithm. Experiment results on FRGC face databases demonstrate the robustness and effectiveness of our method against inaccurate annotation and occlusion. Also,proposed method has improved recognition rates as well as reducing computational cost clearly comparing with several frequently-used linear regression and spectral regression approaches.