  Automatic sign language recognition is an open problem that has received a
lot of attention recently, not only because of its usefulness to signers, but
also due to the numerous applications a sign classifier can have. In this
article, we present a new feature extraction technique for hand pose
recognition using depth and intensity images captured from a Microsoft Kinect
sensor. We applied our technique to American Sign Language fingerspelling
classification using a Deep Belief Network, for which our feature extraction
technique is tailored. We evaluated our results on a multi-user data set with
two scenarios: one with all known users and one with an unseen user. We
achieved 99% recall and precision on the first, and 77% recall and 79%
precision on the second. Our method is also capable of real-time sign
classification and is adaptive to any environment or lightning intensity.
