## 5 Conclusions  
In this study, an unsupervised, encoder-decoder model that is adversarially trained with skip-connections for age classification from CCTV data is proposed. The CNN sub-network of the proposed model extracts local features, and the DCN sub-network extracts global features of the input image. The potency of the parallel model is explained through the ablation study of the entropy images of sub-networks. The Latent vectors of these sub-networks are concatenated and become the input of the decoder, where the input image is reconstructed through transpose convolution. Both CNN and DCN networks have skip connections with the decoder-counterpart, assisting in the vanishing gradient problem. Afterward, the reconstructed image and the input image are input into the discriminator, where they are classified as real or fake. In the unsupervised training scheme, the model is trained with only normal-designated classes without labels. During the inference, the normal-designated classes and the anomaly-designated class is sent through the model, and the AUC and anomaly scores are calculated. The proposed model achieves up to 5% higher AUC score, 2% higher classification accuracy when fine-tuned, and an average of 1.568 lower FID score for three classes (child, adult, and elderly) than the next-best benchmark model. For future works, various methods for improving the proposed deep learning model will be investigated.  
Conceptualization, H.B.B. and J.P.; methodology, H.B.B. and J.P.; software, H.B.B; validation, J.P. and I.F.I; formal analysis, H.B.B., J.P., and I.F.I.; investigation, H.B.B., J.P., and I.F.I.; resources, J.P.; data curation, H.B.B.; writing--original draft preparation, H.B.B.; writing--review and editing, H.B.B. and J.P.; visualization, H.B.B.; supervision, J.P. and I.F.I.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the BB21 plus funded by Busan Metropolitan City and Busan Institute for Talent & Lifelong Education (BIT) and Special Research Fund 2020 of Kyungsung University.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Kyungsung University.  
The surveillance dataset was obtained from the Electronics and Telecommunications Research Institute (ETRI), which is a South Korean government-funded research institute. Therefore, the subject consent used in the research was waived.  
The raw dataset was obtained from the Electronics and Telecommunications Research Institute (ETRI), South Korea. Due to the privacy laws regarding the protection of the recorded individuals with the surveillance cameras, the dataset used in this study is not publicly available.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Classes** & **CNN** & **DCN** & **Parallel Model** \\ \hline Adult & 2.43782 & 2.82346 & 1.61425 \\ Elderly & 2.37259 & 2.90523 & 1.59523 \\ Child & 2.50224 & 2.91293 & 1.63562 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Entropy values of the class activation maps (CAMs) from CNN and DCN modules and the parallel model. The entropy values and the classification accuracy have a correlation on classification accuracy, caused by the unpredictability, which can be seen in Table 5.  
**Acknowledgments:** The surveillance data used in this experiment was obtained from the Electronics and Telecommunications Research Institute (ETRI), South Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.