Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah
Keywords:
gender classification, transfer learning, CNN
Abstract
The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning.
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References
[1] Buchala, S., Davey, N., Frank, R. J., Gale, T. M., Loomes, M. J., and Kanargard, W. “Gender classification of face images: The role of global and feature-based information”. In International Conference on Neural Information Processing. pp. 763-768, 2004. https://doi.org/10.1007/978-3-540-30499-9_117
[2] Budiarto, J., & Qudsi, J. “Deteksi Citra Kendaraan Berbasis Web Menggunakan Javascript Framework Library”. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol.18 no.1, pp.125-133, 2018. https://doi.org/10.30812/matrik.v18i1.325
[3] Triwijoyo, B. K. “Segmentasi Citra Pembuluh Darah Retina Menggunakan Metode Deteksi Garis Multi Skala”. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 15, pp.1, pp.13-19, 2015. https://doi.org/10.30812/matrik.v15i1.28
[4] Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., and Zhang, G. “Transfer learning using computational intelligence: a survey”. Knowledge-Based Systems, no. 80, pp. 14-23, 2015. https://doi.org/10.1016/j.knosys.2015.01.010
[5] Fukushima K and Miyake S. “Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition”. In Competition and cooperation in neural nets pp. 267-285, 1982. https://doi.org/10.1007/978-3-642-46466-9_18
[6] LeCun Y, Boser B, Denker J.S, Henderson D, Howard R.E, Hubbard W, and Jackel L.D. “Backpropagation applied to handwritten zip code recognition. Neural computation”, 1989 https://doi.org/10.1162/neco.1989.1.4.541
[7] Stathakis D. “How many hidden layers and nodes?”. International Journal of Remote Sensing, vol. 30, no.8, pp. 2133-2147, 2009 https://doi.org/10.1080/01431160802549278
[8] Cottrell, G.W., Metcalfe, J. “EMPATH: Face, emotion, and gender recognition using holons”. In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3 (NIPS). Morgan Kaufmann, pp. 564–571, 1990 https://dl.acm.org/citation.cfm?id=2986843
[9] Golomb, B.A., Lawrence, D.T., Sejnowski, T.J. “SEXNET: A neural network identifies sex from human faces”. In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3 (NIPS). Morgan Kaufmann, pp. 572–579, 1990. https://dl.acm.org/citation.cfm?id=2986844
[10] Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C., “Face recognition by elastic bunch graph matching”. In: Sommer, G., Daniilidis, K., Pauli, J. (Eds.), 7th International Conference on Computer Analysis of Images and Patterns, CAIP’97, Kiel. Springer-Verlag, Heidelberg, pp. 456–463, 1997. https://doi.org/10.1007/3-540-63460-6_150
[11] Tamura, S., Kawai, H., Mitsumoto, H. “Male/female identification from 8 to 6 very low resolution face images by neural network”. Pattern Recognition, vol. 29, no.2, pp. 331–335, 1996. https://doi.org/10.1016/0031-3203(95)00073-9 [12] Lyons, M., Budynek, J., Plante, A., Akamatsu, S. “Classifying facial attributes using a 2-d Gabor wavelet representation and discriminant analysis”. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FG’00), IEEE, Grenoble, France, pp. 202–207, 2000. https://doi.org/10.1109/AFGR.2000.840635
[13] Sun, Z., Bebis, G., Yuan, X., Louis, S.J., December. “Genetic feature subset selection for gender classification: A comparison study”. In: Proc. IEEE Workshop on Applications of Computer Vision (WACV’02), pp. 165–170, 2002. https://doi.org/10.1109/ACV.2002.1182176
[14] Jain, A., Huang, J. “Integrating independent components and linear discriminant analysis for gender classification}. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR’04), pp. 159–163, 2004. https://doi.org/10.1109/AFGR.2004.1301524
[15] Costen, N., Brown, M., Akamatsu, S. “Sparse models for gender classification”. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR’04), pp. 201–206, 2004. https://doi.org/10.1109/AFGR.2004.1301531
[16] Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L. “Gender classification based on boosting local binary pattern”. In: Proc. 3rd Internat. Symposium on Neural Networks (ISNN’06), Chengdu, China, vol. 2, pp. 194–201, 2006. https://doi.org/10.1007/11760023_29
[17] Lian, H.-C., Lu, B.-L. “Multi-view gender classification using local binary patterns and support vector machines”. In: Proc. 3rd Internat. Sympos. on Neural Networks (ISNN’06), Chengdu, China, vol. 2, pp. 202–209, 2006. https://doi.org/10.1007/11760023_30
[18] Baluja, S., Rowley, H.A. “Boosting sex identification performance”. Internat. J. Comput. Vision vol. 71, no.1, pp.111–119, 2007. https://doi.org/10.1007/s11263-006-8910-9
[19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks” in Advances in neural information processing systems, 2012, pp. 1097-1105, 2012. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networ
[20] Y. Wen, Z., Li, Y. Qiao, “Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition”, in IEEE Conference on Computer Vision and Pattern Recognition, 2016. http://openaccess.thecvf.com/content_cvpr_2016/html/Wen_Latent_Factor_Guided_CVPR_2016_paper.html
[21] F. Nian, L. Li, T. Li, “Robust gender classification on unconstrained face images”, in ACM International Conference on Internet Multimedia Computing and Service, 2015, pp. 77. https://dl.acm.org/citation.cfm?id=2808570
[22] Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face atributes in the wild”, in IEEE International Conference on Computer Vision, 2015, pp. 3730-3738. http://openaccess.thecvf.com/content_iccv_2015/html/Liu_Deep_Learning_Face_ICCV_2015_paper.html
[23] R. Olga, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, F. Li, “Imagenet large scale visual recognition challenge”, International Journal of Computer Vision, 2015, vol. 115, no. 3, pp. 211-252. https://doi.org/10.1007/s11263-015-0816-y
[24] G. Levi, T. Hassner, “Age and Gender Classification Using Convolutional Neural Network”, in IEEE Conference on Computer Vision and Pattern Recogni- tion Workshops, 2015, pp. 34-42. https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/html/Levi_Age_and_Gender_2015_CVPR_paper.html [25] Eidinger, E., Enbar, R., Hassner, T. “Age and gender estimation of unfiltered faces”. In: Trans. on Inf. Forensics and Security. IEEE, pp. 2170–2179, 2014. https://doi.org/10.1109/TIFS.2014.2359646
[26] Antipov, G., Berrani, S.A., Dugelay, J.L. “Minimalistic CNN-based ensemble model for gender prediction from face images”. In: Pattern Recognition Letters. S. 59-65, 2016. https://doi.org/10.1016/j.patrec.2015.11.011
[27] Huang, G.B. “Labeled faces in the wild: A database for studying face recognition in unconstrained environments”. University of Massachusetts, Amherst. Pp. 7-49, 2007. https://hal.inria.fr/inria-00321923/
[28] Mansanet, J., Albiol, A., Paredes, R. “Local deep neural networks for gender recognition”. In: Pattern Recognition Letters. pp. 80-86, 2016. https://doi.org/10.1016/j.patrec.2015.11.015
[29] Pan, S.J. and Q. Yang. “A survey on transfer learning”. IEEE Transactions on Knowledge and Data Engineering, vol. 22, no.10, pp. 1345-1359, 2010. https://doi.org/10.1109/TKDE.2009.191
[30] Hubel, D.H. and T.N. “Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex”. Journal of Physiology, vol. 160, no. 1, pp. 106-154, 1962. https://doi.org/10.1113/jphysiol.1962.sp006837
[31] Ahmed, A., K. Yu, W. Xu, Y. Gong and E. Xing, “Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks”, in Computer Vision–ECCV, Springer. pp. 69-82, 2008. https://doi.org/10.1007/978-3-540-88690-7_6
[32] Huang, J.T., J. Li, D. Yu, L. Deng and Y. Gong, “Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers”. in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. https://doi.org/10.1109/ICASSP.2013.6639081
[2] Budiarto, J., & Qudsi, J. “Deteksi Citra Kendaraan Berbasis Web Menggunakan Javascript Framework Library”. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol.18 no.1, pp.125-133, 2018. https://doi.org/10.30812/matrik.v18i1.325
[3] Triwijoyo, B. K. “Segmentasi Citra Pembuluh Darah Retina Menggunakan Metode Deteksi Garis Multi Skala”. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 15, pp.1, pp.13-19, 2015. https://doi.org/10.30812/matrik.v15i1.28
[4] Lu, J., Behbood, V., Hao, P., Zuo, H., Xue, S., and Zhang, G. “Transfer learning using computational intelligence: a survey”. Knowledge-Based Systems, no. 80, pp. 14-23, 2015. https://doi.org/10.1016/j.knosys.2015.01.010
[5] Fukushima K and Miyake S. “Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition”. In Competition and cooperation in neural nets pp. 267-285, 1982. https://doi.org/10.1007/978-3-642-46466-9_18
[6] LeCun Y, Boser B, Denker J.S, Henderson D, Howard R.E, Hubbard W, and Jackel L.D. “Backpropagation applied to handwritten zip code recognition. Neural computation”, 1989 https://doi.org/10.1162/neco.1989.1.4.541
[7] Stathakis D. “How many hidden layers and nodes?”. International Journal of Remote Sensing, vol. 30, no.8, pp. 2133-2147, 2009 https://doi.org/10.1080/01431160802549278
[8] Cottrell, G.W., Metcalfe, J. “EMPATH: Face, emotion, and gender recognition using holons”. In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3 (NIPS). Morgan Kaufmann, pp. 564–571, 1990 https://dl.acm.org/citation.cfm?id=2986843
[9] Golomb, B.A., Lawrence, D.T., Sejnowski, T.J. “SEXNET: A neural network identifies sex from human faces”. In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3 (NIPS). Morgan Kaufmann, pp. 572–579, 1990. https://dl.acm.org/citation.cfm?id=2986844
[10] Wiskott, L., Fellous, J.-M., Krüger, N., von der Malsburg, C., “Face recognition by elastic bunch graph matching”. In: Sommer, G., Daniilidis, K., Pauli, J. (Eds.), 7th International Conference on Computer Analysis of Images and Patterns, CAIP’97, Kiel. Springer-Verlag, Heidelberg, pp. 456–463, 1997. https://doi.org/10.1007/3-540-63460-6_150
[11] Tamura, S., Kawai, H., Mitsumoto, H. “Male/female identification from 8 to 6 very low resolution face images by neural network”. Pattern Recognition, vol. 29, no.2, pp. 331–335, 1996. https://doi.org/10.1016/0031-3203(95)00073-9 [12] Lyons, M., Budynek, J., Plante, A., Akamatsu, S. “Classifying facial attributes using a 2-d Gabor wavelet representation and discriminant analysis”. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FG’00), IEEE, Grenoble, France, pp. 202–207, 2000. https://doi.org/10.1109/AFGR.2000.840635
[13] Sun, Z., Bebis, G., Yuan, X., Louis, S.J., December. “Genetic feature subset selection for gender classification: A comparison study”. In: Proc. IEEE Workshop on Applications of Computer Vision (WACV’02), pp. 165–170, 2002. https://doi.org/10.1109/ACV.2002.1182176
[14] Jain, A., Huang, J. “Integrating independent components and linear discriminant analysis for gender classification}. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR’04), pp. 159–163, 2004. https://doi.org/10.1109/AFGR.2004.1301524
[15] Costen, N., Brown, M., Akamatsu, S. “Sparse models for gender classification”. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR’04), pp. 201–206, 2004. https://doi.org/10.1109/AFGR.2004.1301531
[16] Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L. “Gender classification based on boosting local binary pattern”. In: Proc. 3rd Internat. Symposium on Neural Networks (ISNN’06), Chengdu, China, vol. 2, pp. 194–201, 2006. https://doi.org/10.1007/11760023_29
[17] Lian, H.-C., Lu, B.-L. “Multi-view gender classification using local binary patterns and support vector machines”. In: Proc. 3rd Internat. Sympos. on Neural Networks (ISNN’06), Chengdu, China, vol. 2, pp. 202–209, 2006. https://doi.org/10.1007/11760023_30
[18] Baluja, S., Rowley, H.A. “Boosting sex identification performance”. Internat. J. Comput. Vision vol. 71, no.1, pp.111–119, 2007. https://doi.org/10.1007/s11263-006-8910-9
[19] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks” in Advances in neural information processing systems, 2012, pp. 1097-1105, 2012. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networ
[20] Y. Wen, Z., Li, Y. Qiao, “Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition”, in IEEE Conference on Computer Vision and Pattern Recognition, 2016. http://openaccess.thecvf.com/content_cvpr_2016/html/Wen_Latent_Factor_Guided_CVPR_2016_paper.html
[21] F. Nian, L. Li, T. Li, “Robust gender classification on unconstrained face images”, in ACM International Conference on Internet Multimedia Computing and Service, 2015, pp. 77. https://dl.acm.org/citation.cfm?id=2808570
[22] Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face atributes in the wild”, in IEEE International Conference on Computer Vision, 2015, pp. 3730-3738. http://openaccess.thecvf.com/content_iccv_2015/html/Liu_Deep_Learning_Face_ICCV_2015_paper.html
[23] R. Olga, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, F. Li, “Imagenet large scale visual recognition challenge”, International Journal of Computer Vision, 2015, vol. 115, no. 3, pp. 211-252. https://doi.org/10.1007/s11263-015-0816-y
[24] G. Levi, T. Hassner, “Age and Gender Classification Using Convolutional Neural Network”, in IEEE Conference on Computer Vision and Pattern Recogni- tion Workshops, 2015, pp. 34-42. https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/html/Levi_Age_and_Gender_2015_CVPR_paper.html [25] Eidinger, E., Enbar, R., Hassner, T. “Age and gender estimation of unfiltered faces”. In: Trans. on Inf. Forensics and Security. IEEE, pp. 2170–2179, 2014. https://doi.org/10.1109/TIFS.2014.2359646
[26] Antipov, G., Berrani, S.A., Dugelay, J.L. “Minimalistic CNN-based ensemble model for gender prediction from face images”. In: Pattern Recognition Letters. S. 59-65, 2016. https://doi.org/10.1016/j.patrec.2015.11.011
[27] Huang, G.B. “Labeled faces in the wild: A database for studying face recognition in unconstrained environments”. University of Massachusetts, Amherst. Pp. 7-49, 2007. https://hal.inria.fr/inria-00321923/
[28] Mansanet, J., Albiol, A., Paredes, R. “Local deep neural networks for gender recognition”. In: Pattern Recognition Letters. pp. 80-86, 2016. https://doi.org/10.1016/j.patrec.2015.11.015
[29] Pan, S.J. and Q. Yang. “A survey on transfer learning”. IEEE Transactions on Knowledge and Data Engineering, vol. 22, no.10, pp. 1345-1359, 2010. https://doi.org/10.1109/TKDE.2009.191
[30] Hubel, D.H. and T.N. “Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex”. Journal of Physiology, vol. 160, no. 1, pp. 106-154, 1962. https://doi.org/10.1113/jphysiol.1962.sp006837
[31] Ahmed, A., K. Yu, W. Xu, Y. Gong and E. Xing, “Training hierarchical feed-forward visual recognition models using transfer learning from pseudo-tasks”, in Computer Vision–ECCV, Springer. pp. 69-82, 2008. https://doi.org/10.1007/978-3-540-88690-7_6
[32] Huang, J.T., J. Li, D. Yu, L. Deng and Y. Gong, “Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers”. in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. https://doi.org/10.1109/ICASSP.2013.6639081
Published
2019-05-29
How to Cite
Triwijoyo, B. (2019). Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 18(2), 211-221. https://doi.org/https://doi.org/10.30812/matrik.v18i2.376
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