Visual Recognition of Isolated Swedish Sign Language Signs

We present a method for recognition of isolated Swedish Sign Language signs. The method will be used in a game intended to help children training signing at home, as a complement to training with a teacher. The target group is not primarily deaf children, but children with language disorders. Using sign language as a support in conversation has been shown to greatly stimulate the speech development of such children. The signer is captured with an RGB-D (Kinect) sensor, which has three advantages over a regular RGB camera. Firstly, it allows complex backgrounds to be removed easily. We segment the hands and face based on skin color and depth information. Secondly, it helps with the resolution of hand over face occlusion. Thirdly, signs take place in 3D; some aspects of the signs are defined by hand motion vertically to the image plane. This motion can be estimated if the depth is observable. The 3D motion of the hands relative to the torso are used as a cue together with the hand shape, and HMMs trained with this input are used for classification. To obtain higher robustness towards differences across signers, Fisher Linear Discriminant Analysis is used to find the combinations of features that are most descriptive for each sign, regardless of signer. Experiments show that the system can distinguish signs from a challenging 94 word vocabulary with a precision of up to 94% in the signer dependent case and up to 47% in the signer independent case.