Isolated Sign Language Recognition with Fast Hand Descriptors

Recognition of sign language, the main mode of communication of the hearing impaired, has attracted the attention of researchers working in the field of computer vision in recent years. In this study, we propose a fast alternative method to the Improved Dense Trajectories (IDT) method for sign language recognition. In our proposed method, Histogram of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms (MBH) are obtained from the cropped hand regions. Then, Fisher Vectors (FV) are coded and used in classification with Linear Support Vector Machine (SVM) for each descriptor from each sign video. It has been shown that our method can achieve similar performance ten times faster than IDT.