跳到主要內容

臺灣博碩士論文加值系統

(44.200.194.255) 您好!臺灣時間:2024/07/15 01:24
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:林子淵
研究生(外文):Tzu-Yuan Lin
論文名稱:利用深度學習辨識貓臉
論文名稱(外文):Cat face recognition using deep learning
指導教授:郭彥甫
口試委員:鄭文皇花凱龍王尚麟
口試日期:2018-06-22
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:生物產業機電工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:20
中文關鍵詞:卷積神經網絡人臉識別支持向量機
相關次數:
  • 被引用被引用:0
  • 點閱點閱:673
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在台灣,每年有3千多隻的狗和貓走失,丟失寵物對於主人來說是非常難過的,這些走失的寵物也可能會對動物收容所造成負擔。儘管植入寵物晶片一直是解決走失寵物問題的方法之一,但它可能會給寵物帶來健康問題(例如發炎反應和癌症)。因此,我們需要一種非侵入式的走失寵物識別方法。本研究利用非侵入式的臉部辨識方法來識別個體貓,拍攝了一個包含150隻不同的個體貓、共900張貓照片的數據集,使用卷積神經網絡偵測臉上的五官(例如眼睛、鼻子和嘴巴), 將五官的特徵利用特徵臉(Eigenface)量化後,使用支持向量機(Support Vector Machine)來進行辨識。本研究提出的方法達到了94.1%的識別準確度。
In Taiwan, there are more than 3 thousand dogs and cats missing every year. Losing pets could be extremely painful for owners. It also places burden on animal shelters in trying to return the pets to the owners. Although implanting microchips has always been a way to solve the missing pets problem, it may cause health problems (e.g., inflammatory reaction and cancer) to pets. Hence, a noninvasive approach for identifying missing pets is needed. This work proposed to identify cats noninvasively using face recognition. A database that contains 900 images of 150 different cats was developed. Facial parts (e.g., eyes, nose, and mouth) were identified using convolutional neural networks. The features of the facial parts (e.g., eigenface) were then qualified and were used for identifying the cats with support vector machines. The proposed method achieves an identification accuracy of 94.1 %..
TABLE OF CONTENTS
ACKNOWLEDGEMENTS i
摘要 ii
ABSTRACT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1. INTRODUCTION 1
1.1 Background 1
1.2 Objectives 1
1.3 Organization 2
CHAPTER 2. LITERATURE REVIEW 3
2.1 Traditional methods of cat face detection 3
2.2 Advanced detection approaches using deep learning 3
2.3 Conventional researches of cat identification 4
CHAPTER 3. MATERIALS AND METHODS 5
3.1 Dataset preparation 5
3.2 Face detection and facial parts localization 6
3.3 Face alignment 8
3.4 Facial feature extraction 9
3.5 Feature representation using Eigenface 10
3.6 Cat identification 11
CHAPTER 4. RESULTS 12
4.1 The loss of the face detection and facial parts localization models during training 12
4.2 The performance of the developed face detection and facial parts localization models 13
4.3 The performance of cat identification 15
CHAPTER 5. CONCLUSION 17
REFERENCES 18
Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79.
Chen, Y. C., Hidayati, S. C., Cheng, W. H., Hu, M. C., & Hua, K. L. (2016, January). Locality constrained sparse representation for cat recognition. In International Conference on Multimedia Modeling (pp. 140-151). Springer, Cham.
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2), 303-338.
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
Girshick, R. (2015). Fast r-cnn. arXiv preprint arXiv:1504.08083.
Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678). ACM.
Knerr, S., Personnaz, L., & Dreyfus, G. (1990). Single-layer learning revisited: a stepwise procedure for building and training a neural network. In Neurocomputing (pp. 41-50). Springer, Berlin, Heidelberg.
Kozakaya, T., Ito, S., Kubota, S., & Yamaguchi, O. (2009, November). Cat face detection with two heterogeneous features. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 1213-1216). IEEE.
Kumar, S., & Singh, S. K. (2014). Biometric recognition for pet animal. Journal of Software Engineering and Applications, 7(05), 470.
Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 807-814).
Parkhi, O. M., Vedaldi, A., Zisserman, A., & Jawahar, C. V. (2012, June). Cats and dogs. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 3498-3505). IEEE.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91-99).
Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3), 519-524.
Yamada, A., Kojima, K., Kiyama, J., Okamoto, M., & Murata, H. (2011, January). Directional edge-based dog and cat face detection method for digital camera. In Consumer Electronics (ICCE), 2011 IEEE International Conference on (pp. 87-88). IEEE.
Zeiler, M. D., & Fergus, R. (2014, September). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833). Springer, Cham.
Zhang, W., Sun, J., & Tang, X. (2008, October). Cat head detection-how to effectively exploit shape and texture features. In European Conference on Computer Vision (pp. 802-816). Springer, Berlin, Heidelberg.
Zhu, M. (2004). Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, 2, 30.
Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry, 39(4), 561-577.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top