Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7274
標題: 用於未校準人臉影像之基於多模式資訊融合的性別辨識系統
Gender recognition based on multi-model information fusion for unaligned facial images
作者: 張佑瑞
Chang, Yu-Jui
關鍵字: Gender classification;性別分類;Face rectification;Information fusion;人臉校正;資訊融合
出版社: 電機工程學系所
引用: [1] P. Viola and M. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. [2] C.-Y. Yu, Y.-C. Ouyang, C.-M. Wang and C.-I Chang, “Adaptive inverse hyperbolic tangent algorithm for dynamic contrast adjustment in displaying scenes,” EURASIP Journal on Advanced in Signal Processing, vol.2010, pp.1-20, 2010. [3] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol.13, pp. 600-612, 2004. [4] Y. Andreu and R. A. Mollineda, “The role of face parts in gender recognition,” in Proc. LNCS Int'l Conf. on Image Analysis and Recognition, vol.5112, pp. 945-954, 2008. [5] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, pp. 971-987, 2002. [6] R. C. Gonzalea and R. E. Woods, Digital Image Processing, 2nd. Addision-Wesley, 1992. [7] D.-Y. Chen and K.-Y. Lin, “Robust gender recognition for uncontrolled environment of real-life images,” IEEE Transactions on Consumer Electronics, vol.56, pp.1586-1592, 2010. [8] C. Garcia and G. Tziritas, “Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis,” IEEE Transactions on Multimedia, vol.1, pp.264-277, 1999. [9] G. Amayeh, G. Bebis, and M. Nicolescu, “Gender classification from hand shape,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, vol.1, pp. 1-7, 2008. [10] H. Harb and C. Liming, “Gender identification using a general audio classifier,” in Proc. IEEE Int'l Conf. on Multimedia and Expo, vol.2, pp. 733-736, 2003. [11] G. Tzanetakis, “Audio-based gender identification using bootstrapping,” IEEE Pacific Rim Conference on Communications, Computers and signal Processing, pp. 432-433, 2005. [12] Y. Shiqi, T. Tieniu, H. Kaiqi, J. Kui and W. Xinyu, “A Study on Gait-Based Gender Classification,” IEEE Transactions on Image Processing, vol.18, pp. 1905-1910, 2009. [13] M. Hu, Y. Wang, Z. Zhang and D. Zhang, “Gait-Based Gender Classification Using Mixed Conditional Random Field,” IEEE Transactions on Systems, Man, and Cybernetics, vol.PP, pp. 1-11, 2011. [14] S. Aji, T. Jayanthi and M.R. Kaimal, “Gender identification in face images using KPCA,” IEEE Conference on Nature & Biologically Inspired Computing, pp. 1414-1418, 2009. [15] A. Hadid and M. Pietikainen, “Combining motion and appearance for gender classification from video sequences,” IEEE Int'l Conf. on Pattern Recognition, pp. 1-4, 2008. [16] K. Ramesha, N. Srikanth, K.B. Raja, K.R. Venugopal and L.M. Patnaik, “Advanced Biometric Identification on Face, Gender and Age Recognition,” IEEE Int'l Conf. on Advances in Recent Technologies in Communication and Computing, pp. 23-27, 2009. [17] Z. Chair and P.K. Varshney, “Optimal Data Fusion in Multiple Sensor Detection Systems,” IEEE Transactions on Aerospace and Electronic Systems, vol.AES-22, pp. 98-101, 1986. [18] R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, 2001. [19] H. V. Poor, An introduction to Signal Detection and Estimation, Springer, 1994. [20] V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995. [21] H. Lu, Y. Huang, Y. Chen, and D. Yang, “Automatic gender recognition based on pixel-pattern-based texture feature,” Journal of Real-Time Image Processing, vol. 3, pp. 109-116, 2008. [22] K. I. Laws, “Rapid texture identification,” In SPIE, Image Processing for Missile Guidance, vol. 238, pp.376-380, 1980. [23] N. Ansari, E.S.H. Hou, B.-O. Zhu and J.-G. Chen, “Adaptive fusion by reinforcement learning for distributed detection systems,” IEEE Transactions on Aerospace and Electronic Systems, vol.32, pp. 524-531, 1996. [24] W.-N. Lie and C.-K. Su, “News video classification based on multi-modal information fusion,” in Proc. IEEE Int'l Conf. on Image Processing, vol.1, pp. 11-14 , 2005. [25] A. Cellerino, D. Borghetti, and F. Sartucci, “Sex differences in face gender recognition in humans,” Brain Research Bulletin, vol. 63, pp. 443-449, 2004. [26] H. Fukai, H. Takimoto, Y. Mitsukura and M. Fukumi, “Age and Gender Estimation System based on Human Perception,” Proc. of 18th IEEE International Symposium on Robot and Human Interactive Communication, pp.1143-1148, 2009. [27] http://www.csie.ntu.edu.tw/~cjlin/
摘要: 
於本論文,我們提出一個基於多模式資訊融合的性別辨識系統。而所提出的性別辨識系統主要由四個部分所構成:人臉偵測與校正、人眼偵測、特徵擷取和性別分類器。為了驗證所提出系統的效能,我們使用low cost webcam拍攝大量不同大小人臉的實驗影像。實驗結果顯示我們提出的方法除了可以正確地找出人臉並且能準確找到人眼位置。此外,所提出的系統性別辨識率可達到95%。這些結果驗證我們所提出的方法除了人臉偵測之外,也可以達到性別辨識的效果。

In the thesis, we proposed a gender recognition scheme based on multi-model information fusion. The proposed gender recognition scheme is composed of four parts: face detection and rectification, eye detection, feature extraction, and gender classifier. To evaluate the proposed scheme, a large number of images containing different-size faces are captured by using low-cost webcam. Experimental results show that our proposed scheme can detect facial regions as well as eyes well. In addition, the accuracy of gender recognition for our scheme is more than 95%. These results demonstrate that our proposed scheme can achieve not only face detection but also gender recognition.
URI: http://hdl.handle.net/11455/7274
其他識別: U0005-2907201114521700
Appears in Collections:電機工程學系所

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