A mixture of gated experts optimized using simulated annealing for 3D face recognition

A commonly accepted fact in the biometrics related domain is that fusing multiple classifiers for decision making generally leads to improved recognition performance. Meanwhile, the search for an optimal fusion strategy remains extraordinarily complex since the cardinality of the space of possible fusion schemes is exponentially proportional to the number of competing classifiers. In this paper, we propose a mixture of gated experts for 3D face recognition using an ensemble of 24 different classifiers. The mixture of gated experts is optimized using a Simulated Annealing-based algorithm. It automatically selects and fuses the most relevant similarity measurements. The experimental results of 3D face recognition achieved on the FRGC v2.0 dataset illustrate the effectiveness and stability of the proposed method. Additionally, as a learning-based method, it also has a good robustness to the variations of training database.