Deep convolutional neural network-based Leveraging Lion Swarm Optimizer for gesture recognition and classification

Abstract

Vision-based human gesture detection is the task of forecasting a gesture, namely clapping or sign language gestures, or waving hello, utilizing various video frames. One of the attractive features of gesture detection is that it makes it possible for humans to interact with devices and computers without the necessity for an external input tool like a remote control or a mouse. Gesture detection from videos has various applications, like robot learning, control of consumer electronics computer games, and mechanical systems. This study leverages the Lion Swarm optimizer with a deep convolutional neural network (LSO-DCNN) for gesture recognition and classification. The purpose of the LSO-DCNN technique lies in the proper identification and categorization of various categories of gestures that exist in the input images. The presented LSO-DCNN model follows a three-step procedure. At the initial step, the 1D-convolutional neural network (1D-CNN) method derives a collection of feature vectors. In the second step, the LSO algorithm optimally chooses the hyperparameter values of the 1D-CNN model. At the final step, the extreme gradient boosting (XGBoost) classifier allocates proper classes, i.e., it recognizes the gestures efficaciously. To demonstrate the enhanced gesture classification results of the LSO-DCNN approach, a wide range of experimental results are investigated. The brief comparative study reported the improvements in the LSO-DCNN technique in the gesture recognition process.

Keywords

• human-computer interaction
• swarm intelligence
• cnn model
• gesture recognition
• transfer learning