Multiple Face Tracking using Kalman and Hungarian Algorithm to Reduce Face Recognition Computational Cost

Willy Achmat Fauzi, Supeno M Susiki Nugroho, Eko Mulyanto Yuniarno, Wiwik Anggraeni, Mauridhi Hery Purnomo

Abstract

Currently, research in face recognition systems
mainly utilized deep learning to achieve high accuracy. Using
deep learning as the base platform, per frame image processing
to detect and recognize faces is computationally expensive,
especially for video surveillance systems using large numbers of
mounted cameras simultaneously streaming video data to the
system. The idea behind this research is that the system does not
need to recognize every occurrence of faces in every frame. We
used MobileNet SSD to detect the face, Kalman filter to predict
face location in the next frame when detection fails, and
Hungarian algorithm to maintain the identity of each face.
Based on the result, using our algorithm 87.832 face that must
be recognized is reduced to only 204 faces, and run at the realtime scenario. This method is proven to be used in surveillance
systems by reducing computational cost.

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