EBGM vs subspace projection for face recognition

Autonomic human-machine interfaces need to determine the user of the machine in a non-obtrusive way. The identification of the user can be done in many ways, using RF ID tags, the audio stream or the video stream to name a few. In this paper we focus on the identification of faces from the video stream. In particular, we compare two different approaches, linear subspace projection from the appearance-based methods, and Elastic Bunch Graph Matching from the feature-based. Since the intended application is restricted to indoor multi-camera setups with collaborative users, the deployment scenarios of the recognizer are easily identified. The comparison of the methods is done using a common test-bed for both methods. The test-bed is exhaustive for the deployment scenarios we need to consider, leading to the identification of deployment scenarios for which each method is preferable.