Discriminant Boosted Dynamic Time Warping and Its Application to Gesture Recognition

Dynamic time warping (DTW) measures similarity between two data sequences by minimizing an accumulated distance between two sequence samples at each iteration and a cost is computed to assess the level of the similarity. The DTW cost may then be used to assign a sequence to a class if the problem is a classification problem. In machine learning, classification problems are solved using features with good discrimination power, which are generated by exploiting the distribution of data vectors. Linear Discriminant Analysis (LDA) is such a technique and finds discriminative projection directions which are used to generate features as projections of sequence vectors on to these directions. Unfortunately, these techniques are not applicable to warped sequences because the mapping between the test sequences and the training sequences is not known. To solve this problem, we propose a constrained LDA framework that produces direction vectors that repeat unit vectors that have dimensions equal to the dimensions of a single sequence sample. Such projection vectors can be used without knowing the mapping of test sequence vectors to training sequence vectors. Experiment results show that generating features by discriminant analysis improves the performance significantly.