Abstract
The Mexican Sign Language (LSM) is a language of the deaf Mexican community, which consists of a series of gestural signs articulated by hands and accompanied with facial expressions. The lack of automated systems to translate signs from LSM makes integration of hearing-impaired people to society more difficult. This work presents a new method for LSM alphanumerical signs recognition based on 3D Haar-like features extracted from depth images captured by the Microsoft Kinect sensor. Features are processed with a boosting algorithm. To evaluate performance of our method, we recognized a set of signs from letters and numbers, and compared the results with the use of traditional 2D Haar-like features. Our system is able to recognize static LSM signs with a higher accuracy rate than the one obtained with widely used 2D features.
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