Most discriminating segment - Longest common subsequence (MDSLCS) algorithm for dynamic hand gesture classification

In this work, we consider the recognition of dynamic gestures based on representative sub-segments of a gesture, which are denoted as most discriminating segments (MDSs). The automatic extraction and recognition of such small representative segments, rather than extracting and recognizing the full gestures themselves, allows for a more discriminative classifier. A MDS is a sub-segment of a gesture that is most dissimilar to all other gesture sub-segments. Gestures are classified using a MDSLCS algorithm, which recognizes the MDSs using a modified longest common subsequence (LCS) measure. The extraction of MDSs from a data stream uses adaptive window parameters, which are driven by the successive results of multiple calls to the LCS classifier. In a preprocessing stage, gestures that have large motion variations are replaced by several forms of lesser variation. We learn these forms by adaptive clustering of a training set of gestures, where we reemploy the LCS to determine similarity between gesture trajectories. The MDSLCS classifier achieved a gesture recognition rate of 92.6% when tested using a set of pre-cut free hand digit (0–9) gestures, while hidden Markov models (HMMs) achieved an accuracy of 89.5%. When the MDSLCS was tested against a set of streamed digit gestures, an accuracy of 89.6% was obtained. At present the HMMs method is considered the state-of-the-art method for classifying motion trajectories. The MDSLCS algorithm had a higher accuracy rate for pre-cut gestures, and is also more suitable for streamed gestures. MDSLCS provides a significant advantage over HMMs by not requiring data re-sampling during run-time and performing well with small training sets. Gestures may be the most natural way for humans to communicate with their environment and fellow humans, second only to speech. In recognition of this fact, there is an increased interest, among researchers and industrialists, in the development of digital devices that use hand gesture interfaces as a major mode of interaction. Dynamic hand gestures encode information by their temporal trajectories. They are most commonly found in gesture-based applications using semantic based vocabularies where each gesture encodes a meaning. Gesture classification is a critical component of such gesture recognition systems (GRSs). In a GRS with dynamic gestures we assume that there exists a tracking device (vision based, accelerometer, magnetic, etc.) that calculates a centroidal position of the hand. A sequence of cen-troids constitutes a gesture trajectory from which descriptive features are extracted. These features are fed into the classification module, which either …