Feature selection for hand pose recognition in human-robot object exchange scenario

Abstract

Vision-based hand gesture recognition relies on the extraction of features describing the hand, and the appropriate set of features is usually selected in an empirical manner. We propose in this article a systematic selection of the best features to be considered. An iterative sequential forward feature selection (SFS) approach is proposed to combine the features with the highest recognition rate considering the Gaussian Mixture Modelling within the Expectation Maximization algorithm as classification technique. This approach has been tested with two different illustrative databases. The first one is related to human robot physical interaction and the hand postures considered correspond to key postures the human partner performs just before acquiring an object from the robot. The second database corresponds to the representation of the 10 first numbers of the American Sign Language. In both cases, the recognition rate obtained, measured through the F1 score metrics, is satisfactory (over 0,97), and demonstrates that the proposed technique could be applied to a very large field of applications.

Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts

“…The resulting descriptor set always improves the quality of the classification comparing to the best descriptor by its own. This selection techniques can be extended to different datasets and contexts as proved within this paper and previous ones [34][35][36].…”

“…As stated before, the crux of the matter in this paper relies on how to select the different visual features to improve the individual score of each descriptor. Some authors have used different techniques to do this [34,35]. Feature Selection is a machine learning technique that is used in many fields and usually improves the accuracy of the model.…”

“…In [34], the authors uses different feature selection techniques to improve the score in the Quantitative Structure-Activity Relationship (QSAR). In [35], instead, they use a similar approach for hand pose recognition. In [36], a view over the different feature selection techniques and its variations is described.…”