Improving the Performance of CNN by Using Dominant Patterns of CNN for Hand Detection
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Abstract
Many applications have used hand gestures for software interaction, image- and video-based action analysis, and behavioral monitoring. Hand detection is an essential step in the pipeline of these applications, and Convolutional Neural Networks (CNN) has provided superior solutions. However, CNN has similar features between hand and non-hand images, called non-dominant features. These features affect miss-classifications and long-time computation. Therefore, this paper focuses on the selection of dominant CNN features for hand detection, and it is our proposed method (DP-CNN) that selects the dominant feature patterns (DP) from the trained CNN features and classifies them using the Extreme Learning Machine (ELM) method. Evaluation results show the proposed method (DP-CNN-ELM), which can increase the accuracy and the F1-score of CNN. In addition, the proposed method can reduce the time computation of CNN in training and testing.
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