Abstract
Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area. The footstep signals classification process for security systems still has a low level of accuracy. Therefore, we need a classification system that has a high accuracy for security systems. Most systems are generally developed using geometric and holistic features but still provide high error rates. In this research, a new system is proposed by using the Mel Frequency Cepstral Coefficients (MFCCs) feature extraction, because it has a good linear frequency as a copycat of the human hearing system and Artificial Neural Network (ANN) as a classification algorithm because it has a good level of accuracy with a dataset of 500 recording footsteps. The classification results show that the proposed system can achieve the highest accuracy of validation loss value 57.3, Accuracy testing 92.0%, loss value 193.8, and accuracy training 100%, the accuracy results are an evaluation of the system in improving the foot signal recognition system for security systems in the smart home environment.
Highlights
Footstep recognition is relatively new biometrics and based on the learning of footsteps signals captured from people walking on the sensing area
Research Methodology environment by applying the The system is designed to consist of a process to the data Mel Frequency Cepstral Coefficients (MFCCs) feature extraction method and Artificial Neural Network (ANN) algorithm split or data separation process
After getting the model, research [12][13] a study was carried out using MFCCs the testing process is carried out using test data by feature extraction to translate speech into digital text applying ANN classification that have been obtained in with an accuracy of around 87.5%
Summary
[2], data shows that the occurrence of crime continues every year. The rise of these actions caused unrest, Every human being has different characteristics, one of which made the community must be wary of home which is his footsteps. Research Methodology environment (people are more specific/in accordance with the characteristics of his footsteps) by applying the The system is designed to consist of a process to the data MFCCs feature extraction method and ANN algorithm split or data separation process. After getting the model, research [12][13] a study was carried out using MFCCs the testing process is carried out using test data by feature extraction to translate speech into digital text applying ANN classification that have been obtained in with an accuracy of around 87.5%. After carrying out these tests it will get the results of the accuracy of the footstep recognition this research proposes the feature extraction system process.
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