On accuracy/robustness/complexity trade-offs in face verification

In much of the literature devoted to face recognition, experiments are performed with controlled images (e.g. manual face localization, controlled lighting, background and pose). However, a practical recognition system has to be robust to more challenging conditions. In this paper, we first evaluate on the relatively difficult BANCA database, the discrimination accuracy, robustness and complexity of Gaussian mixture model (GMM), 1D- and pseudo-2D hidden Markov model (HMM) based systems, using both manual and automatic face localization. We also propose to extend the GMM approach through the use of local features with embedded positional information, increasing accuracy without sacrificing its low complexity. Experiments show that good accuracy on manually located faces is not necessarily indicative of good accuracy on automatically located faces (which are imperfectly located). The deciding factor is shown to be the degree of constraints placed on spatial relations between face parts. Methods which utilize rigid constraints have poor robustness compared to methods which have relaxed constraints. Furthermore, we show that while the pseudo-1D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach.