The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa.To fill this gap,this study implements a CNN-based neural network,Convolutional Based Attention Module(CBAM),t...The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa.To fill this gap,this study implements a CNN-based neural network,Convolutional Based Attention Module(CBAM),to recognise Malaysian Sign Language(MSL)in videos recognition.This study has created 2071 videos for 19 dynamic signs.Two different experiments were conducted for dynamic signs,using CBAM-3DResNet implementing‘Within Blocks’and‘Before Classifier’methods.Various metrics such as the accuracy,loss,precision,recall,F1-score,confusion matrix,and training time were recorded to evaluate the models’efficiency.Results showed that CBAM-ResNet models had good performances in videos recognition tasks,with recognition rates of over 90%with little variations.CBAMResNet‘Before Classifier’is more efficient than‘Within Blocks’models of CBAM-ResNet.All experiment results indicated the CBAM-ResNet‘Before Classifier’efficiency in recognising Malaysian Sign Language and its worth of future research.展开更多
文摘The deaf-mutes population is constantly feeling helpless when others do not understand them and vice versa.To fill this gap,this study implements a CNN-based neural network,Convolutional Based Attention Module(CBAM),to recognise Malaysian Sign Language(MSL)in videos recognition.This study has created 2071 videos for 19 dynamic signs.Two different experiments were conducted for dynamic signs,using CBAM-3DResNet implementing‘Within Blocks’and‘Before Classifier’methods.Various metrics such as the accuracy,loss,precision,recall,F1-score,confusion matrix,and training time were recorded to evaluate the models’efficiency.Results showed that CBAM-ResNet models had good performances in videos recognition tasks,with recognition rates of over 90%with little variations.CBAMResNet‘Before Classifier’is more efficient than‘Within Blocks’models of CBAM-ResNet.All experiment results indicated the CBAM-ResNet‘Before Classifier’efficiency in recognising Malaysian Sign Language and its worth of future research.
