# Diverse Pose Lip-Reading Framework
## 5 Experimental Results
### Comparison  
The performance, in terms of accuracy, of multiple poses has been compared on the MP dataset with existing methods [20; 21; 26] as illustrated in Table 5. All the methods, except the proposed DNN (ResNet) method, show a decreasing trend in accuracy with the increasing view angle. The accuracy of the method [20] is the lowest among all the methods  
\begin{table}
\begin{tabular}{c l} \hline \hline
**S.No** & **Sentences** \\ \hline
1 & YOU JUST HAVE TO FIND ANOTHER TERM AND LOOK THAT UP \\ \hline
2 & I DO LIKE MAGIC \\ \hline
3 & WE REALLY DON’T WALK ANYMORE \\ \hline
4 & AT SOME POINT I’M GOING TO GET OUT \\ \hline
5 & WHEN I GET OUT OF THIS AM I GOING TO BE REJECTED \\ \hline
6 & WE’D LOVE TO HELP \\ \hline \hline \end{tabular}
\end{table}
Table 4: The proposed method accurately recognized these sentences.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Poses**} & \multicolumn{3}{c}{**MP dataset**} & \multicolumn{3}{c}{**MV-LRS**} \\ \cline{2-7}  & **CER** & **WER** & **BLEU** & **CER** & **WER** & **BLEU** \\ \hline Frontal & 42.1\% & 51.0\% & 48.4 & 46.5\% & 56.4\% & 49.3 \\ \hline
15\({}^{\circ}\) & 45.5\% & 53.7\% & 50.2 & 49\% & 56\% & 47 \\ \hline
30\({}^{\circ}\) & 49.7\% & 57\% & 45.8 & 50.4\% & 59.2\% & 46.1 \\ \hline
45\({}^{\circ}\) & 46\% & 55.6\% & 45.8 & 50.4\% & 59.2\% & 46.1 \\ \hline
60\({}^{\circ}\) & 48\% & 55\% & 46.1 & 50.4\% & 59.2\% & 46.1 \\ \hline
75\({}^{\circ}\) & 53.7\% & 60.1\% & 46.1 & 53.2\% & 62.3\% & 45.8 \\ \hline
90\({}^{\circ}\) & 53\% & 59.3\% & 41.4 & 54.4\% & 62.8\% & 42.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance measure of two datasets for all possible poses.  
and decreases rapidly with increasing view angle. The method in [21] shows a relatively better accuracy and decreases gradually with increasing view angle. A high accuracy of 90.1% is shown by the method in [26] for frontal faces. The effect of view angle on the accuracy of this method is relatively less compared to the method in [21]. Compared to all the mentioned methods, the accuracy of the proposed framework is superior in the frontal and side view angles. It can be noted that the accuracy of all the other methods decreases with the increasing view angle, but the accuracy of the proposed framework remains stable and does not vary much. The reason is the fact that our proposed framework is equipped with the CLAHE method and FF-Gan. Due to the applications of these two methods, the proposed framework is getting frontalized face images with better contrast, which in turn enables it to localize the mouth portion on the face images with accuracy better than the prior methods. The sentence prediction accuracy of the proposed DNN (ResNet) is comparatively better because of training on an accurate sequence of mouth frames extracted from each speaker dependent video. Furthermore, all the methods in [20; 21; 26] can process either frontal faces video frames, videos containing isolated words and videos that have been recorded in a specific environment or high quality videos. Our proposed framework improved the accuracy of sentence recognition from the speaker dependent lip movements. However, its sentence recognition accuracy is not satisfactory for unseen speaker lip movements. Furthermore, the FF-Gan results are not up-to-the-mark for faces with hair-like mustaches and beards, which in turn affect the recognition accuracy of our proposed framework for spoken words of people with mustaches and/or beards.