Hand Gesture Recognition Using Semi Vectorial Multilevel Segmentation Method with Improved ReliefF Algorithm

Currently, hand gesture recognition becomes a promising system for the digital entertainment field due to the recent development of sensor innovations and machine learning. However, hand gesture recognition is a challenging task in most of the existing models, due to background clutter, motion blur, illumination variations, and occlusions. In this research, a dynamic hand gesture recognition system is proposed to improve the performance of hand gesture recognition. At first, the normalization method is used to enhance the visibility level of the gesture images which are collected from the Indian sign language dataset. Then, semi vectorial multilevel segmentation method is employed to segment the exact gesture regions from the normalized images. Further, improved reliefF algorithm and K-nearest neighbour classifiers are used to select the optimal features and to classify the 16 gesture classes or symbols. In the experimental phase, the proposed improved reliefFK-nearest neighbour model performance is analysed in light of Matthew’s correlation coefficient, accuracy, sensitivity, specificity, and f-score. For overall 16 distinct alphabets; A, B, D, E, F, G, H, K, P, R, T, U, W, X, Y, Z, the proposed improved reliefF-K-nearest neighbour model achieves average accuracy of 98.95%. Hence, the proposed improved reliefF-K-nearest neighbour model showed maximum of 7.97% and minimum of 0.15% improvement in recognition accuracy compared to k means-neural network model, and discriminant correlation analysis based unimodal feature-level fusion model.

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