Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. An algorithm that performs detection, extraction, and evaluation of these facial expressions will allow for automatic recognition of human emotion in images and videos. Presented here is a hybrid feature extraction and facial expression recognition method that utilizes Viola-Jones cascade object detectors and Harris corner key-points to extract faces and facial features from images and uses principal component analysis, linear discriminant analysis, histogram-of-oriented-gradients (HOG) feature extraction, and support vector machines (SVM) to train a multi-class predictor for classifying the seven fundamental human facial expressions.
The hybrid approach allows for quick initial classification via projection of a testing image onto a calculated eigenvector, of a basis that has been specifically calculated to emphasize the separation of a specific emotion from others. This initial step works well for five of the seven emotions which are easier to distinguish. If further prediction is needed, then the computationally slower HOG feature extraction is performed and a class prediction is made with a trained SVM.
Reasonable accuracy is achieved with the predictor, dependent on the testing set and test emotions. Accuracy is 81% with contempt, a very difficult-to-distinguish emotion, included as a target emotion and the run-time of the hybrid approach is 20% faster than using the HOG approach exclusively.
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