# Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification
## 4 Results and Discussion
### Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **SHSW** & **SHTW** & **Walk** & **Standing** & **BHSW** & **BHTW** & **Total Images** & **Recall** & **Classification** & **Un-Classified** & **Theal** & **In-Classification** \\ \hline _SHSW_ & **21** & 0 & 0 & 0 & 0 & 0 & 21 & 100\% & 0\% & 3 & 24 & 12.9\% \\ _SHTW_ & 0 & **82** & 0 & 2 & 0 & 0 & 54 & 96.3\% & 37.5\% & 0 & 54 & 0\% \\ _Walk_ & 0 & 0 & **4** & **53** & 0 & 0 & 61 & 86.9\% & 13.1\% & 2 & 63 & 3.2\% \\ _BHTW_ & 0 & 0 & 0 & 0 & **3** & 34 & 91.2\% & 8.8\% & 4 & 38 & 10.5\% \\ _BHTW_ & 0 & 0 & 0 & 0 & 0 & **40** & 40 & 100\% & 0\% & 0 & 40 & 0 \\ _Total_ & 21 & 56 & 53 & 65 & 31 & 0 & \(\pm\) 20 & \(\pm\) 20 & & 9 & m - 278 & Overall Un Classification(4,4\%) \\ _Precision_ & 100\% & 92.9\% & 92.9\% & 81.5\% & 100\% & 93.0\% & Overall Accuracy(91.4\%) & & & & Overall Un-Classification(4,4\%) \\ _Error_ & 0.0\% & 7.1\% & 7.5\% & 18.5\% & 0.0\% & 7.0\% & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification matrix for Drai images. Table 6 is showing classification matrix results for the actions performed by \"Ido\". The original dataset contained 196 images having all six action images. Out of them, 10 images were not classified. For the images where the proposed technique is able to classify, the overall accuracy is 89.2%. It may be observed that the proposed technique achieved 88.5%, 80.8%, 85.4%, 93.5%, 96.4%, and 89.5% recall, and 100%, 77.8%, 100%, 78.6%, 100%, and 94.4% precision for the SHSW, SHTW, walk, standing, BHSW, and BHTW actions, respectively. The classification matrix for \"Lena\" images is presented in Table 7 obtained by testing the proposed technique over a total of 214 action images. The number of unclassified images is 9 while 205 have been successfully classified. The overall accuracy for \"Lena\" images is 93.7%. The matrix shows that the proposed technique achieved 100%, 91.3%, 97.1%, 85.7%, 91.7%, and 95.7% recall, and 100%, 77.8%, 100%, 90%, 95.7%, and 91.7% precision for SHSW, SHTW, walk,  
The classification matrix for the images of actions performed by \"Moshe\" is represented in Table 9. There were a total of 242 images, out of whom 11 images couldn't get classified through the proposed technique while the overall accuracy for the remaining 231 images is 87.4%. The precision and recall for SHSW, SHTW, walk, standing, BHSW, and BHTW are 100%, 61.9%, 100%, 75%, 100%, and 92.9%, and 89.3%, 86.7%, 97.3%, 71.7%, 90.5%, and 83.9% respectively. Table 10 is representing the classification matrix for the complete dataset. The same as the classification matrices for the individual actors, it is showing the statistical results obtained by implementing the proposed technique for different parameters, namely; precision, recall, overall accuracy, overall Un-Classification, commission error, and omission error. The dataset contained 1855 images from all the actors having all the six actions. The proposed technique is unable to classify 84 actions while the remaining 1771 are classified with an overall accuracy of 91.4%. The recall values are 95.7%, 90.3%, 90.5%, 88.9%, 94.1%, and 92.3%, and the precision values are 98.6%, 84%, 97.7%, 81.3%, 99.4%, and 94.2% for SHSW, SHTW, walk, standing, BHSW, and BHTW, respectively. Figure 6 is showing the performance of the proposed technique using precision, recall, and F-score metrics. The statistics are presented for SHSW, SHTW, Walk, Standing, BHSW, and BHTW. The proposed technique has been applied over the Weizmann Dataset for all the performance parameters. In case of precision, it achieved 0.98, 0.84, 0.98, 0.81, 0.99, and 0.94, in case of recall; 0.96, 0.90, 0.91, 0.89, 0.94, and 0.92 while in the case of F-score; 0.97, 0.87, 0.94, 0.85, 0.97, and 0.93 have been achieved for SHSW, SHTW, walk, standing, BHSW, and BHTW actions, respectively. The highest precision has been achieved both in the case of SHSW and BHSW while standing has the least precision value of 0.81.