Linear and nonlinear feature-based fusion algorithms for face recognition

Integration of various face recognition algorithms has proved to be a feasible approach to improve the performance of a face recognition system. Different face recognition algorithms are often based on different representations of the input patterns or on extracted features and hence may complement each other. Linear and nonlinear feature based algorithms can capture and handle different kinds of variations, such as pose, illumination and expression variations. To make full use of the different advantages of different classifiers, we propose combining four linear and nonlinear face recognition algorithms via a weighted combination scheme to improve the recognition performance of a face recognition system. The FERET, YaleB and CMU PIE database are used for evaluating the combination scheme and the results confirm the effectiveness of the proposed combination scheme.