Paper Title :Combination of Long-Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) Algorithms for Classifying Hand Gestures

Author :Maricel L. Amit, Arnel C. Fajardo, Ruji P. Medina

Article Citation :Maricel L. Amit ,Arnel C. Fajardo ,Ruji P. Medina , (2019 ) " Combination of Long-Short Term Memory (LSTM) and Multi-Layer Perceptron (MLP) Algorithms for Classifying Hand Gestures " , International Journal of Management and Applied Science (IJMAS) , pp. 140-145, Volume-5,Issue-9

Abstract : The study aimed to enhance the accuracy of the hand motion and gestures acknowledgment by utilizing the Long Short-Term Memory (LSTM), a feature extraction and Multi-layer Perceptron (MLP) as the classifier with regards to the classification of hand gestures to test the accuracy of the method compared to the other algorithms such as the LSTM+CNN (LCNN), CNN-RNN, VGG16, and DCNN+MCSVM. In this paper, a novel LSTM+MLP model is presented on the classification for American Sign Language (ASL) hand signs composed of a total of 400 images. Furthermore, based on the findings of the study, the model LSTM+MLP outperformed the previous algorithms which gained a 100% accuracy rate during the training and testing as shown in the tables, illustrations and graphs. Keywords - Classification, Long-Short Term Memory, Multi-layer Perceptron, Neural Networks

Type : Research paper

Published : Volume-5,Issue-9

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