Facial Action Unit Recognition using Filtered Local Binary Pattern Features with Bootstrapped and Weighted ECOC Classi ers

Within the context face expression classi cation using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). The method adopted is to train a single error-correcting output code (ECOC) multiclass classi er to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to calibrate the ECOC outputs to probabilities and appropriate sums of these probabilities are taken to obtain a separate probability for each AU individually. Feature extraction is performed by generating a large number of local binary pattern (LBP) features and then selecting from these using fast correlationbased ltering (FCBF). The bias and variance properties of the classi er are measured and we show that both these sources of error can be reduced by enhancing ECOC through the application of bootstrapping and class-separability weighting.