Enhanced Face Recognition using Data Fusion
Alaa Eleyan · International Journal of Intelligent Systems and Applications · 2012
In this paper we scrutinize the influence of fusion on the face recognition performance.In pattern recognition task, benefit ing fro m d ifferent uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance.In features fusion approach, we fuse (concatenate) the feature vectors obtained using different feature extractors for the same image.Classification is then performed using different similarity measures.In decisions fusion approach, the fusion is performed at decisions level, where decisions fro m different algorith ms are fused using majority voting.The proposed method was tested using face images having different facial expressions and conditions obtained fro m ORL and FRA V2D databases.Simu lations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.