Boosted multi image features for improved face detection
In this paper, we present novel approaches of automatically detecting human faces in images which is extremely important for any face recognition system. This paper expands on the traditional Viola-Jones approach by proposing to boost a plethora of mixed feature sets for face detection; we do this by adding non-Haar-like elements to a large pool of mixed features in an Adaboost framework. We show how to generate discriminative support vector machine (SVM) type features and Gabor-type features (in various orientations and frequencies and central locations) and use this whole pool as possible discriminative candidate feature sets in modeling the patterns of a frontal view human face. This general and large-diversity pool of features is used to build a boosted strong classifier and we show we can improve the generalization performance of the AdaBoost approach, and as a result improving the robustness of the face detector. We report performance on the MIT+CMU face database and compare the result with other published face detection algorithms. We also discuss processing times and speeding up methods to offset the increase in complexity in order to achieve face detection in real time.