基于Haar特征的Turbo-Boost表情识别算法

Turbo-Boost Facial Expression Recognition Using Haar-Like Features

针对AdaBoost在使用Haar特征时的局限性,提出了Turbo-Boost算法.该算法经过两轮AdaBoost迭代,先从原始的Haar特征空间中筛选出F维主要特征子空间,再从中训练T>F个弱分类器,以进行最终的表情识别.在CAS-PEAL-R1表情库上的10折交叉验证结果表明,Turbo-Boost算法可显著提升识别性能,对微笑、皱眉、惊讶、张口和闭眼5类表情的总体识别准确率达到了93.6%.此外,该算法的识别速度快,可满足实时识别的需要.

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.