The comparative study of model-based and appearance based gait recognition for leave bag behind - Zainol, Norfazilah (2018)
Nowadays, the increasing number of crimes and violence in the world has become a concern of modern society. This is why the need for criminal recognition using gait used for civilian and forensic analysis applications has evoked considerable interest. The literature accurate the result can be found in gait recognition by leave bag behind detection especially in the critical area examples airport and shopping mall environment. This is important because the method used capable of identifying the subject based on their gait and can be presented as the most probable subject as a strong evidence for criminal identification. This research limited to leave the bag behind detection on gait recognition. In this research, the analysis performed using two methods which are Model-Based approaches and Appearance-Based approaches. The selected methods were implemented in MATLAB R2014a and R Studio and tested with a standard dataset from the Chinese Academy of Science (CASIA) and tested using two classifiers which is Support Vector Machine (SVM) and KNN (K nearest Neighbour) based on accuracy and misclassification rates (MER) metrics. The experiment results show that the accuracy and misclassification rate (MER) of Appearance-based approaches obtained is 93.66% and 6.33% respectively tested on SVM classifier then the accuracy and misclassification rate (MER) of Appearancebased approaches is 97.66% and 2.33% respectively tested on KNN algorithm. Meanwhile, the accuracy and misclassification rate (MER) of Model-based approaches obtained is 97.00% and 3.00% respectively tested on SVM classifier then the accuracy and misclassification rate (MER) of Model-based approaches is 99.00% and 1.00% respectively tested on KNN algorithm. It can be concluded from experiments conducted by Model-based approaches better than Appearance-based approaches because Model-Based approaches higher precision value as well as low misclassification.