# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 5 Conclusions and Future Works  
A real-time pre-identification mechanism and a cascaded detector were proposed for tiny-face detection in this paper. We combined pedestrian detection with face detection to reduce the search region. After obtaining the bounding box of the pedestrian, we estimated the face area as RoI based on the proportion of the size of the bounding box. The mechanism not only can improve efficiency but also reduce the number of false positives. After that, we built an image pyramid as the input of the face detector. The algorithm can ensure the performance of the face detector on the targets of different sizes. Based on the experimental results, the proposed cascaded neural network is more efficient in tiny-face detection than other state-of-the-art methods.  
There are many open studies in the above research. Our method is constrained, but when the body is not completely occluded, PMCD can perform better in tiny-face detection under extreme conditions such as poor light and occlusion, etc. Traditional face detection methods and region-based CNNs cannot keep a high accuracy in tiny-face detection by themselves, but it is possible to combine these unconstrained methods with our proposed framework in the future. The highest priority should be given to this framework to avoid unnecessary false positives and reduce the miss rate of tiny-face detection.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**PMCD** & **HR** & **MMSC** & **MTCNN** & **SSD** & **VJ** \\ \hline MIoU & 84.8\% & 83.4\% & 76.9\% & 79.2\% & 71.2\% & 73.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean intersection over union (MIoU) on the self-collected dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VJ** & **MTCNN** & **MMSC** & **SSD** & **HR** & **PMCD** \\ \hline FPS & 1.2 & 32.8 & 3.9 & 43.2 & 2.6 & 34.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The F1 measures of different methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VJ** & **MTCNN** & **MMSC** & **SSD** & **HR** & **PMCD** \\ \hline FPS & 1.2 & 32.8 & 3.9 & 43.2 & 2.6 & 34.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection speed of different methods.  
As mentioned in Section 1, cascade frameworks have significant advantages in detecting tiny faces, and the experimental results also support this conclusion in Section 4. This is because the characteristics of tiny faces are gradually lost with convolutional layers going deeper. Therefore, this problem can be solved by finding a larger upper-body to complete the coarse-to-fine detection. PMCD has achieved remarkable results on our self-collected dataset when the targets are small or in extreme environments such as darkness, complex background, occlusion, etc. When the targets are of normal sizes, the end-to-end detection frameworks can obtain a faster speed. Therefore, PMCD still has potential for further improvement. In the future, we will design an end-to-end detection network based on PMCD, which can internally extract hierarchical features for the face detection task. This network can ensure satisfactory performance on tiny-face detection while keeping a faster detection speed than PMCD.  
All authors of the paper made significant contributions to this work. [PERSON] and [PERSON] contributed equally to this paper, conceived the idea of work, implemented algorithms, analyzed the experiment data, and wrote the manuscript. [PERSON] led the project, directed and revised the paper writing. [PERSON] helped to code and analysis the experiment data.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61762061, 61963027, and 61703198), the Natural Science Foundation of Jiangxi Province, China (Grant No. 20161 ACB20004), Natural Science Foundation for Distinguished Young Scholars of Jiangxi Province (Grant No. 2018 ACB21014), and Jiangxi Key Laboratory of Smart City (Grant No. 20192 BCD40002).  
The authors declare no conflict of interest.