Optimisation of both classifier and fusion based feature set for static American sign language recognition

Abstract

Sign language recognition becomes a popular research field in human-computer interaction. Attention on hand signal analysis helps to make easy communication among computer and human for information sharing. Major focus of the gesture recognition system is to identify and recognise various gestures, by a computer. This study introduces optimisation of both classifier and feature set for static American sign language recognition. Initially, the hand part is segmented from other parts of the image through effective edge and skin colour detection. Thereafter, robust features are obtained using discrete cosine transform, Zernike moment, scale-invariant feature transform, speeded-up robust features, histogram of oriented gradients and binary object features from the segmented hand image. From these extracted features, an optimal feature set is selected by social ski driver optimisation algorithm. Deep Elman recurrent neural network classifier is then introduced for recognition purpose. Optimisation is performed on feature sets, derived by fusion of features obtained from the above methods, based on precision, accuracy, F-measure and recall. Finally, optimised feature set and best classifier are used to recognise the hand gesture for classification purpose. The performance of this proposed method is evaluated and compared with existing literature.