Turbo-Boost facial expression recognition using Haar-like features

To overcome the limitation of AdaBoost algorithm on Haar-like features, Turbo-Boost algorithm is proposed in this paper. Our proposed algorithm has a 2-iteration AdaBoost training framework. In the first iteration, An F-dimension principal feature subspace is selected. In the second iteration, a strong classifier constructed of T>F weak classifiers is trained in the F-dimension subspace. A 10 folds cross-validation on the CAS-PEAL-R1 facial expression database shows that Turbo-Boost outperforms AdaBoost significantly with a 93.6% overall precision on 5 categories of facial expressions including smiling, frowning, surprising, mouse opening, and eyes closing. Furthermore, Turbo-Boost algorithm is fast and suitable for real time applications.