請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57672完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均(Jian-Jung Ding) | |
| dc.contributor.author | Min-Chen Hsu | en |
| dc.contributor.author | 許銘宸 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:57:13Z | - |
| dc.date.available | 2020-07-27 | |
| dc.date.copyright | 2020-07-27 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-22 | |
| dc.identifier.citation | [1] Angeloni, Marcus, Rodrigo de Freitas Pereira, and Helio Pedrini. 'Age Estimation From Facial Parts Using Compact Multi-Stream Convolutional Neural Networks.' Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019. [2] Baltrusaitis, Tadas, et al. 'Openface 2.0: Facial behavior analysis toolkit.' 2018 13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018). IEEE, 2018. [3] Wang, Fei, et al. 'Residual attention network for image classification.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [4] Peng, Xingchao, et al. 'Moment matching for multi-source domain adaptation.' Proceedings of the IEEE International Conference on Computer Vision. 2019. [5] Ganin, Yaroslav, et al. 'Domain-adversarial training of neural networks.' The Journal of Machine Learning Research 17.1 (2016): 2096-2030. [6] He, Kaiming, et al. 'Deep residual learning for image recognition.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [7] Simonyan, Karen, and Andrew Zisserman. 'Very deep convolutional networks for large-scale image re cognition.' arXiv preprint arXiv:1409.1556 (2014) [8] Rothe, Rasmus, Radu Timofte, and Luc Van Gool. 'Deep expectation of real and apparent age from a single image without facial landmarks.' International Journal of Computer Vision 126.2-4 (2018): 144-157. [9] Fg-net. The Fg-net Aging Database. Available from: http://wwwprima.inrialpes.fr/FGnet/html/benchmarks.html. [Accessed 19th March 2014] [10] Ponce-López, Víctor, et al. 'Chalearn lap 2016: First round challenge on first impressions-dataset and results.' European conference on computer vision. Springer, Cham, 2016. [11] https://commons.wikimedia.org/wiki/File:Max_pooling.png [12] Lecture of HungYi, Lee http://speech.ee.ntu.edu.tw/~tlkagk/ courses_ML17_2.html [13] Zhang, Kaipeng, et al. 'Joint face detection and alignment using multitask cascaded convolutional networks.' IEEE Signal Processing Letters 23.10 (2016): 1499-1503. [14] Russakovsky, Olga, et al. 'Imagenet large scale visual recognition challenge.' International journal of computer vision 115.3 (2015): 211-252., [15] Wang, Hee Lin, et al. 'Effects of facial alignment for age estimation.' 2010 11th International Conference on Control Automation Robotics Vision. IEEE, 2010 [16] https://en.wikipedia.org/wiki/RGB_color_space [17] Wang, Hee Lin, et al. 'Effects of facial alignment for age estimation.' 2010 11th International Conference on Control Automation Robotics Vision. IEEE, 2010. [18] https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/ [19] Geng, Xin, Zhi-Hua Zhou, and Kate Smith-Miles. 'Automatic age estimation based on facial aging patterns.' IEEE Transactions on pattern analysis and machine intelligence 29.12 (2007): 2234-2240. [20] Kwon, Young H., and Niels da Vitoria Lobo. 'Age classification from facial images.' Computer vision and image understanding 74.1 (1999): 1-21. [21] Yang, Tsun-Yi, et al. 'SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation.' IJCAI. Vol. 5. No. 6. 2018. [22] Hu, Jie, Li Shen, and Gang Sun. 'Squeeze-and-excitation networks.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [23] Shen, Wei, et al. 'Deep regression forests for age estimation.' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. [24] Szegedy, Christian, et al. 'Inception-v4, inception-resnet and the impact of residual connections on learning.' Thirty-first AAAI conference on artificial intelligence. 2017. [25] Snell, Karen B. 'Age-related changes in temporal gap detection.' The Journal of the Acoustical Society of America 101.4 (1997): 2214-2220. [26] Levi, Gil, and Tal Hassner. 'Age and gender classification using convolutional neural networks.' Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2015. [27] Hsu, Rein-Lien, Mohamed Abdel-Mottaleb, and Anil K. Jain. 'Face detection in color images.' IEEE transactions on pattern analysis and machine intelligence 24.5 (2002): 696-706. [28] Yang, Shuo, et al. 'Wider face: A face detection benchmark.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [29] Cao, Xudong, et al. 'Face alignment by explicit shape regression.' International Journal of Computer Vision 107.2 (2014): 177-190. [30] Kazemi, Vahid, and Josephine Sullivan. 'One millisecond face alignment with an ensemble of regression trees.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. [31] Niu, Zhenxing, et al. 'Ordinal regression with multiple output cnn for age estimation.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. [32] Karthikeyan, D., and G. Balakrishnan. 'A comprehensive age estimation on face images using hybrid filter based feature extraction.' (2018). [33] Han, Hu, et al. 'Demographic estimation from face images: Human vs. machine performance.' IEEE transactions on pattern analysis and machine intelligence 37.6 (2014): 1148-1161. [34] Chen, Ke, et al. 'Cumulative attribute space for age and crowd density estimation.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2013. [35] Guo, Guodong, et al. 'Image-based human age estimation by manifold learning and locally adjusted robust regression.' IEEE Transactions on Image Processing 17.7 (2008): 1178-1188. [36] K.-Y. Chang, C.-S. Chen, and Y.-P. Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In CVPR, 2011. [37] Wan, Jun, et al. 'Auxiliary demographic information assisted age estimation with cascaded structure.' IEEE transactions on cybernetics 48.9 (2018): 2531-2541. [38] Liu, Xin, et al. 'Agenet: Deeply learned regressor and classifier for robust apparent age estimation.' Proceedings of the IEEE International Conference on Computer Vision Workshops. 2015. [39] Liu, Hao, et al. 'Group-aware deep feature learning for facial age estimation.' Pattern Recognition 66 (2017): 82-94. [40] Rothe, Rasmus, Radu Timofte, and Luc Van Gool. 'Dex: Deep expectation of apparent age from a single image.' Proceedings of the IEEE international conference on computer vision workshops. 2015. [41] https://medium.com/@syshen/%E5%85%A5%E9%96%80%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-2-d694cad7d1e5 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57672 | - |
| dc.description.abstract | 在科技日益發達的21世紀，物聯網、大數據等科技蓬勃發展，年齡辨識也是可以結合在其中的一項便民科技，不管是結合到家聯網或是警網犯罪監控系統都是很好的應用。不僅如此，年齡辨識的領域應用越來越廣泛,除了上述所提之外，還可以應用到便利商店菸酒的未成年警示以及汽車無照駕駛之提醒。搭配現在的物聯網,年齡辨識可以獲得實務上的應用,例如:保全巡邏,居家照護,以及教育娛樂場所。 本論文提出的方法有三個階段，第一階段是資料預處理，我們利用雙重偵測器對照片做人臉偵測，將偵測後的結果進行裁切，接著將裁切過後的人臉進行校正，最後微調人臉的亮度以及對比度。第二階段是我們的深度捲積層，在這一階段中有特徵提取模型、雙重分類模型。首先，將預處理好的照片輸入到我們的模型之中進行特徵抽取，而我們的特徵抽取模型是基於attention機制的Residual Attention Model。特徵提取器會輸出1024維的Embedding 當作下一個分類模型的輸入，雙重分類模型會先將照片分為10類，分別是0~10~20~30~40~50~60~70~80~90~100，接著由分類的結果送到各自的第二分類模型，這裡將會預測出照片與該類平均的差值。最後，藉由該組平均與預測出的差值做計算得到實際預測的年齡。 我們採用了IMDB當作訓練集，WIKI當作驗證集，最後在FG-Net及LAP 資料集做測試。由實驗結果可以看到我們所提出的架構有效降低辨識的錯誤率。 | zh_TW |
| dc.description.abstract | In the 21st century where technology is increasingly developed, technologies such as the Internet of Things (IoT) and big data are booming. Age recognition is also a convenient technology that can be integrated into it. Whether it is integrated into a home network or a police network crime monitoring system. The method proposed in this paper has three stages. The first stage is data preprocessing. We use dual detectors to detect faces in the photo, crop the detected results, and then align the cropped faces. Finally, fine-tune the brightness and contrast of the face. The second stage is our deep convolutional layer. In this stage, there are a feature extraction model and a dual classification model. First, input the preprocessed photos into our model for feature extraction, and our feature extraction model is the Residual Attention Model based on the attention mechanism. The feature extractor will output 1024-dimensional embedding with the input image as the input of the next classification model. The dual classification model will first divide the photos into 10 categories, which are 0~10~20~30~40~50~60~70~80~90~100, and then the results of the classification are sent to the respective second classification model, where the difference between the photo and the average of this category will be predicted. Finally, the actual predicted age is calculated by the difference between the average and the predicted value of the group. We used IMDB as the training set, WIKI as the verification set, and finally tested on the FG-Net and LAP datasets. From the experimental results, we can see that the proposed architecture effectively reduces the estimation error rate. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:57:13Z (GMT). No. of bitstreams: 1 U0001-1707202017142500.pdf: 2892509 bytes, checksum: 1657f75cb7d208998062abefffa30f75 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Convolutional Neural Network 2 2.1 Convolutional Neural Network 2 2.1.1 Introduction 2 2.1.2 Convolution Layer 3 2.1.3 Pooling Layer 4 2.1.4 Batch Normalization 5 2.1.5 Fully Connected Layer 6 Chapter 3 Proposed Method 7 3.1 Introduction 7 3.2 Pre-processing 10 3.2.1 Face Detection 10 3.2.2 Alignment and Feature Mapping 17 3.3 Feature Extraction 25 3.3.1 DEX[8] 27 3.3.2 VGG Model [7] 28 3.3.3 ResNet152 Model [6] 29 3.3.4 Residual Attention Model [3] 32 3.3.5 Joint Loss 34 3.4 Domain Adaptation 35 3.4.1 Introduction 35 3.4.2 DANN [5] 38 3.4.3 Application 38 3.5 Double Classification and Post-processing 39 Chapter 4 Facial Stream Model 42 4.1 Related work 42 4.2 Pre-processing 44 4.3 5-stream CNN Training 44 Chapter 5 Simulation Results 46 5.1 Database 46 5.2 Compare with Existing Algorithm 50 5.3 Conclusion and Future work 52 REFERENCE 53 | |
| dc.language.iso | en | |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 年齡辨識 | zh_TW |
| dc.subject | 人臉偵測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | face detection | en |
| dc.subject | age estimation | en |
| dc.subject | deep learning | en |
| dc.subject | machine learning | en |
| dc.title | 基於結合損失函數及臉部特徵之學習型年齡估測 | zh_TW |
| dc.title | Learning Based Age Estimation Using Joint Loss and Facial Landmarks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王鵬華(Peng-Hua Wang),許文良(Wen-Liang Hsu),歐陽良昱(Liang-Yu Oyang) | |
| dc.subject.keyword | 年齡辨識,深度學習,人臉偵測,機器學習, | zh_TW |
| dc.subject.keyword | face detection,age estimation,deep learning,machine learning, | en |
| dc.relation.page | 57 | |
| dc.identifier.doi | 10.6342/NTU202001609 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-22 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1707202017142500.pdf 未授權公開取用 | 2.82 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。