失蹤兒童沒有被找到的案例雖然少，但仍不斷地在發生。如果沒有立即找到小孩，那麼家人可能會因為長時間沒有見到自己的小孩，導致無法辨認出小孩的外觀。因此我們的目的是要預測兒童長大後的樣貌，協助家人搜尋失蹤兒童。目前最準確檢測任兩人是否有血緣關係的方法是醫療性的親子鑑定方法，但是如果對每一位身分不明兒童都進行醫療性的親子鑑定，那麼耗費的成本會非常大。所以本研究提出「失蹤兒童樣貌預測系統」，可自動預測失蹤兒童在現今年齡的樣貌，使得家人可快速確認自己與任何一位失蹤兒童是否有親子關係的可能性。好處是可縮小搜尋的範圍且低成本。本糸統主要結合StyleGAN2和FaceNet方法來實現預測兒童長大後的樣貌，StyleGAN2用於風格混合兩張人臉圖像，FaceNet用於比對兩張人臉圖像的相似度。最後的實驗證明，本系統的預測結果與預期結果大約有75%以上的相似度，這表示本系統能良好的預測小孩長大後的樣貌。並且本系統相較於CAAE、HRFAE和IPCGAN三種人臉老化模型，有更高的相似性與更自然的結果。

Cases of missing children not being found are rare, but they continue to occur. If the child is not found immediately, the parents may not be able to identify the child's appearance because they have not seen their child for a long time. Therefore, our purpose is to predict children's faces when they grow up and help parents search for missing children. DNA paternity testing is the most accurate way to detect whether two people have a blood relation. However, DNA paternity testing for every unidentified child would be costly. Therefore, we propose the development of the Face Prediction System for Missing Children in a Smart City Safety Network. It can predict the faces of missing children at their current age, and parents can quickly confirm the possibility of blood relations with any unidentified child. The advantage is that it can eliminate incorrect matches and narrow down the search at a low cost. Our system combines StyleGAN2 and FaceNet methods to achieve prediction. StyleGAN2 is used to style mix two face images. FaceNet is used to compare the similarity of two face images. Experiments show that the similarity between predicted and expected results is more than 75%. This means that the system can well predict children's faces when they grow up. Our system has more natural and higher similarity comparison results than Conditional Adversarial Autoencoder (CAAE), High Resolution Face Age Editing (HRFAE) and Identity-Preserved Conditional Generative Adversarial Networks (IPCGAN).

[1] The International Centre for Missing and Exploited Children. Missing Children Statistics. Available online: https://www.icmec.org/missing-children-statistics (accessed on 10 June 2022).
[2] United Nations Development Programme. Human Development Report 2020: The Next Frontier: Human Development and the Anthropocene. New York. Available online: https://hdr.undp.org/content/human-development-report-2020 (accessed on 10 June 2022).
[3] Federal Bureau of Investigation, 2020 NCIC Missing Person and Unidentified Person Statistics. Available online: https://www.fbi.gov/file-repository/2020-ncic-missing-person-and-unidentified-person-statistics.pdf/view (accessed on 10 June 2022).
[4] National Crime Agency, UK Missing Persons Unit. Available online: https://www.missingpersons.police.uk/en-gb/resources/downloads/missing-persons-statistical-bulletins (accessed on 10 June 2022).
[5] National Police Agency, Ministry of Interior. Available online: https://ba.npa.gov.tw (accessed on 10 June 2022).
[6] Child Protection and Family Services Agency, Missing Children Statistics by Sex 2016. Available online: https://www.ocr.gov.jm/index.php/statistics/missing-children/missing-children-2016 (accessed on 10 June 2022).
[7] Child Protection and Family Services Agency, Statistics on the total number of reports received by the NCR by type, sex, month and year, 2007 to 2017. Available online: https://www.ocr.gov.jm/index.php/statistics (accessed on 10 June 2022).
[8] Health Info in Thailand. Available online: https://www.hiso.or.th/hiso/picture/reportHealth/ThaiHealth2021/eng2021_21.pdf (accessed on 10 June 2022).
[9] National Crime Records Bureau, 2019 Missing and Traced Children. Available online: https://ncrb.gov.in/sites/default/files/crime_in_india_table_additional_table_chapter_reports/Table%2015.1_2.pdf (accessed on 10 June 2022).
[10] National Crime Records Bureau, 2020 Missing and Traced Children. Available online: https://ncrb.gov.in/sites/default/files/crime_in_india_table_additional_table_chapter_reports/TABLE%2015.1.pdf (accessed on 10 June 2022).
[11] Ministry of Labour and Social Protectio, Child Protection Report 2016 - 2019. Available online: https://laboursp.go.ke/wp-content/uploads/2021/03/Child-Protection-Report-2016-2019.pdf (accessed on 10 June 2022).
[12] United Nations, Office on Drugs and Crime. Available online: https://www.unodc.org/unodc/en/data-and-analysis/glotip.html (accessed on 10 June 2022).
[13] Alba Keneth Alert. Available online: https://www.albakeneth.gob.gt/index.php/estadisticas (accessed on 10 June 2022).
[14] The United States Department of Justice, Office of Justice Programs, AMBER Alert. Available online: https://amberalert.ojp.gov/statistics (accessed on 10 June 2022).
[15] S. P. Mohanty, U. Choppali, and E. Kougianos, “Everything you wanted to know about smart cities: The Internet of things is the backbone,” in IEEE Consumer Electronics Magazine, vol. 5, no. 3, pp. 60–70, July, 2016.
[16] M. Lacinák, and J. Ristvej, “Smart City, Safety and Security,” Procedia Engineering, vol. 192, pp. 522–527, 2017.
[17] J. Ristvej, M. Lacinák, and R. Ondrejka, “On Smart City and Safe City Concepts,” Mobile Networks and Applications, vol. 25, pp. 836–845, 2020.
[18] Z. Zhang, Y. Song, and H. Qi, “Age Progression/Regression by Conditional Adversarial Autoencoder,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4352–4360, Honolulu, HI, USA, July 21–26, 2017.
[19] S. Palsson, E. Agustsson, and R. Timofte, “Gool, L.V. Generative Adversarial Style Transfer Networks for Face Aging,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2165–21658, Salt Lake City, UT, USA, June 18–22, 2018.
[20] X. Yao, G. Puy, A. Newson, and Y. Gousseau, “Hellier, P. High Resolution Face Age Editing,” in Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 8624–8631, Milan, Italy, January 10–15, 2021.
[21] X. Tang, Z. Wang, W. Luo, and S. Gao, “Face Aging with Identity-Preserved Conditional Generative Adversarial Networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7939–7947, Salt Lake City, UT, USA, June 18–23, 2018.
[22] P. Balasundaram, and I.D. Avulakunta, “Human Growth and Development,” in StatPearls [Internet], Treasure Island (FL): StatPearls Publishing. Available online: https://www.ncbi.nlm.nih.gov/books/NBK567767/ (accessed on 10 June 2022).
[23] The Human Life Cycle. Available online: https://med.libretexts.org/@go/page/1918 (accessed on 10 June 2022).
[24] E. Polan, and D. Taylor, “Journey across the life span: Human development and health promotion,” Philadelphia: F.A. Davis Co, 1998.
[25] A. M. Roberts, “The complete human body: the definitive visual guide,” 2016.
[26] J.M. Tanner, and P.S. Davies, “Clinical longitudinal standards for height and height velocity for North American children. J Pediatr,”, vol. 107, no. 3, pp. 317–329, 1985.
[27] I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, X. Bing, and Y. Bengio, “Generative adversarial networks,” in Proceedings of the Neural Information Processing Systems (NeurIPS), pp. 2672–2680, Montreal, QC, Canada, December 8–13, 2014.
[28] T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” in Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, April 30–May 3, 2018.
[29] T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4401–4410, Long Beach, CA, USA, June 15–20, 2019.
[30] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8107–8116, Seattle, WA, USA, June 13–19, 2020.
[31] R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan: How to embed images into the stylegan latent space?,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4431–4440, Seoul, Korea, October 27–28, 2019.
[32] R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan++: How to edit the embedded images?,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 13–19, 2020.
[33] P. Zhu, R. Abdal, Y. Qin, and P. Wonka, “Sean: Image synthesis with semantic region-adaptive normalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, June13–19, 2020.
[34] E. Collins, R. Bala, and B. Price, “Susstrunk, S. Editing in style: Uncovering the local semantics of gans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, June 13–19, 2020.
[35] R. Abdal, P. Zhu, N. Mitra, and P. Wonka, “StyleFlow: Attribute-Conditioned Exploration of StyleGAN-Generated Images Using Conditional Continuous Normalizing Flows,” ACM Trans. Graph. (TOG), vol. 40, pp. 1–21, 2021.
[36] E. Richardson, Y. Alaluf, O. Patashnik, Y. Nitzan, Y. Azar, S. Shapiro, and D. Cohen-Or, “Encoding in Style: A StyleGAN Encoder for Image-to-Image Translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2287–2296, Nashville, TN, USA, June 20–25, 2021.
[37] O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, and D. Lischinski, “StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery,” arXiv 2021, arXiv:2103.17249.
[38] H. Kim, Y. Choi, J. Kim, S. Yoo, and Y. Uh, “Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 852–861, Nashville, TN, USA, June 20–25, 2021.
[39] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A Unified Embedding for Face Recognition and Clustering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823, Boston, MA, USA, June 7–12, 2015.
[40] G.B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeled Faces in theWild: A Database for Studying Face Recognition in Unconstrained Environments,” Available online: http://vis-www.cs.umass.edu/lfw/lfw.pdf (accessed on 10 June 2022).
[41] T. Baltrušaitis, P. Robinson, and L.-P. Morency, “OpenFace: An open source facial behavior analysis toolkit,” in Proceedings of the IEEE Winter Conference on Application of Computer Vision, pp. 1–10, Lake Placid, NY, USA, March 7–10, 2016.
[42] Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701–1708, Columbus, OH, USA, June 23–28, 2014.
[43] O. Parkhi, A. Vedaldi, and A. Zisserman, “Deep Face Recognition,” in Proceedings of the British Machine Vision Conference (BMVC), pp. 41.1–41.12, Nottingham, UK, September 1–5, 2014.
[44] Q. Cao, L. Shen, W. Xie, O. Parkhi, and A. Zisserman, “VGGFace2: A dataset for recognising faces across pose and age,” in Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 67–74, Xi’an, China, May 15–19, 2018.
[45] Y. Sun, X. Wang, and X. Tang, “Deep Learning Face Representation from Predicting 10,000 Classes,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1891–1898, Columbus, OH, USA, June 23–28, 2014.
[46] Y. Sun, X. Wang, and X. Tang, “Deep Learning Face Representation by Joint Identification-Verification,” in Proceedings of the 27th International Conference on Neural Information Processing Systems—Volume 2 (NIPS’14), pp. 1988–1996, Montreal, QC, Canada, December 8–13, 2014.
[47] Y. Sun, X. Wang, and X. Tang, “Deeply learned face representations are sparse, selective, and robust,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2892–2900, Boston, MA, USA, June 7–12, 2015.
[48] Y. Sun, D. Liang, X. Wang, and X. Tang, “DeepID3: Face Recognition with Very Deep Neural Networks,” arXiv 2015, arXiv:1502.00873.
[49] J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “ArcFace: Additive Angular Margin Loss for Deep Face Recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, June 15–20, 2019.
[50] D.E. King, “Dlib-ml: A Machine Learning Toolkit,” JMLR, vol. 10, pp. 1755–1758, 2009.
[51] M. Yan, M. Zhao, Z. Xu, Q. Zhang, G. Wang, and Z. Su, “VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2647–2654, Seoul, Korea, October 27–28, 2019.
[52] The Internet of Everything How More Relevant and Valuable Connections Will Change the World. Available online: https://www.cisco.com/c/dam/global/en_my/assets/ciscoinnovate/pdfs/IoE.pdf (accessed on 10 June 2022).
[53] Session 2: Pillars of the IoE. Available online: https://www.open.edu/openlearn/mod/oucontent/view.php?id=48819 (accessed on 10 June 2022).
[54] D.P. Kingma, and M. Welling, “Auto-Encoding Variational Bayes,” arXiv 2021, arXiv:1312.6114.
[55] A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” arXiv 2015, arXiv:1511.05644.
[56] X. Bian, and J. Li, “Conditional adversarial consistent identity autoencoder for cross-age face synthesis,” Multimedia Tools and Applications, vol. 80, pp. 14231–14253, 2021.
[57] M. Anwaar, C.K. Loo, and M. Seera, “Face image synthesis with weight and age progression using conditional adversarial autoencoder,” Neural Computing and Applications, vol. 32, pp. 3567–3579, 2020.
[58] J. Zhu, T. Park, P. Isola, and A.A. Efros, “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 2242–2251, Venice, Italy, October 22–29, 2017.
[59] P. Isola, J. Zhu, T. Zhou, and A.A. Efros, “Image-to-Image Translation with Conditional Adversarial Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976, Honolulu, HI, USA, July 21–26, 2017.
[60] Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8789–8797, Salt Lake City, UT, USA, June 18–23, 2018.
[61] Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the Latent Space of GANs for Semantic Face Editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9240–9249, Seattle, WA, USA, June 13–19, 2020.
[62] R. Or-El, S. Sengupta, O. Fried, E. Shechtman, and I. Kemelmacher-Shlizerman, “Lifespan Age Transformation Synthesis,” arXiv 2020, arXiv: 2003.09764.
[63] N. Sharma, R. Sharma, and N. Jindal, “Prediction of face age progression with generative adversarial networks,” Multimedia Tools and Applications, vol. 80, pp. 33911–33935, 2021.
[64] M. Mirza, and S. Osindero, “Conditional generative adversarial nets,” arXiv 2014, arXiv:1411.1784.
[65] H. Yang, D. Huang, Y. Wang, and A. K. Jain, “Learning Face Age Progression: A Pyramid Architecture of GANs,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol. 31–39, 2018.
[66] H. Pranoto, Y. Heryadi, H. Leslie, H. S. Warnars, and W. Budiharto, “Enhanced IPCGAN-Alexnet model for new face image generating on age target,” Journal of King Saud University - Computer and Information Sciences, 2021.
[67] J. Despois, F. Flament, and M. Perrot, “AgingMapGAN (AMGAN): High-Resolution Controllable Face Aging with Spatially-Aware Conditional GANs,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 613–628, 2021.
[68] The Internet of Everything How More Relevant and Valuable Connections Will Change the World. Available online: https: //www.cisco.com/c/dam/global/en_my/assets/ciscoinnovate/pdfs/IoE.pdf (accessed on 10 June 2022).
[69] Session 2: Pillars of the IoE. Available online: https://www.open.edu/openlearn/mod/oucontent/view.php?id=48819 (accessed on 10 June 2022).
[70] SKEye, Face Compare. Available online: https://www.sk-ai.com/Experience/face-compare (accessed on 10 June 2022).