Deep neural learning techniques with long short-term memory for gesture recognition

Abstract

Gesture recognition is a kind of biometric which has assumed great significance in the field of computer vision for communicating information through human activities. To recognize the various gestures and achieve efficient classification, an efficient computational machine learning technique is required. The Shift Invariant Convolutional Deep Structured Neural Learning with Long Short-Term Memory (SICDSNL–LSTM) and Bivariate Fully Recurrent Deep Neural Network with Long Short-Term Memory (BFRDNN–LSTM) have been introduced for classifying human activities with efficient accuracy and minimal time complexity. The SICDSNL–LSTM technique collects gesture data (a kind of biometric) from the dataset and gives it to the input layers of Shift Invariant Convolutional Deep Structured Neural Learning. The SICDSNL–LSTM technique uses two hidden layers for performing regression and classification. In the regression process, dice similarity is used for measuring the relationship between data and output classes. In the second process, the input data are classified into dissimilar classes for each time step using LSTM unit with soft-step activation function. The soft-step activation function uses ‘forget gate’ for removing the less significant data from the cell state. This is also used to make a decision to display the output at a particular time step and to remove other class results. Then, LSTM output is given to the output layers, and convolutional deep neural learning is performed to classify the gesture. Based on the classification results, human activity and gesture are recognized with high accuracy. The BFRDNN–LSTM technique also performs regression in the first hidden layers using bivariate correlation to find relationship between data and classes. The LSTM unit in BFRDNN–LSTM technique uses Gaussian activation function in the second hidden layers for categorizing incoming data into various classes at each time step. In this proposed BFRDNN–LSTM method, fully recurrent deep neural network utilizes gradient descent function to minimize the error rate at the output layers and to increase the accuracy of the gesture recognition. Both SICDSNL–LSTM and BFRDNN–LSTM techniques automatically learn the features and the data to minimize time complexity in gesture recognition. Experimental evaluation is carried out using factors such as gesture recognition accuracy, false-positive rate and time complexity with a number of data.