Using Multiple Masks to Improve End-to-End Face Recognition Performance
In this paper, we propose a method to improve performance in end to end face recognition systems. Our system uses a combination of masks that extract different regions of the face, and performs recognition separately on each region. Individual mask results are then combined through weighted sums and Borda voting. We test the method in conjunction with the fisherfaces (LDA) algorithm, and we analyze performance in an end-to-end system where face recognition is preceded by face and then eye detection using the Viola Jones algorithm. We find that our method improves recognition results by almost 10% on both manual and automatically detected face and eye locations.
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