Abstract
Automatic Gender Classification (AGC) is an essential problem due to its growing demand in commercial applications, including social media and security environments such as the airport. AGC is a well-researched topic both in the field of Computer Vision and Biometrics. In this paper, we propose the use of decision-level fusion for AGC in videos. Our approach does a decision-level fusion of labels obtained from two fine-tuned deep-networks based on a color image and optical-flow image, respectively, based on Resnet-18 architecture. We compare our proposed method with handcrafted features, which includes the concatenation of the Histogram of Optical Flow (HOF) and Histogram of Oriented Gradients (HOG). We compare it with deep-networks, which includes pre-trained & fine-tuned Resnet-18 based on a color image, and pre-trained & fine-tuned Resnet-18 based on optical flow image. Our fusion-based approach considerably outperforms both the handcrafted features, and the deep-networks previously mentioned. Another advantage of our proposed method is that it can work when the visual features are hidden. We used 98 videos from the HMDB51 action recognition dataset, specifically from the cart-wheel action with an almost 50% training, and testing split without validation set. We achieve an overall accuracy of 79.59% with Resnet-18 network architecture with the proposed method, compared to fine-tuned single-stream Resnet-18 for Color-Stream at 65.30%, and Optical-Flow at 55.10% respectively.
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References
Bagher Oskuie, F., Faez, K.: Gender classification using a novel gait template: radon transform of mean gait energy image. In: Kamel, M., Campilho, A. (eds.) Image Analysis and Recognition, pp. 161–169. Springer, Berlin Heidelberg, Berlin, Heidelberg (2011)
Bourdev, L., Maji, S., Malik, J.: Describing people: a poselet-based approach to attribute classification. In: 2011 International Conference on Computer Vision, pp. 1543–1550. IEEE (2011)
Chen, J., Liu, S., Chen, Z.: Gender classification in live videos. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 1602–1606. IEEE (2017)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). https://doi.org/10.1007/11744047_33
Dantcheva, A., Elia, P., Ross, A.: What else does your biometric data reveal? a survey on soft biometrics. IEEE Trans. Inf. Forensics Secur. 11(3), 441–467 (2015)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504. http://www.sciencedirect.com/science/article/pii/S002200009791504X
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07–49, University of Massachusetts, Amherst, October 2007
Huh, M., Agrawal, P., Efros, A.A.: What makes imagenet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016)
Kannala, J., Rahtu, E.: BSIF: binarized statistical image features. In: Proceeding of 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, pp. 1363–1366 (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012). http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)
Liew, S.S., Hani, M.K., Radzi, S.A., Bakhteri, R.: Gender classification: a convolutional neural network approach. Turkish J. Electr. Eng. Comput. Sci. 24(3), 1248–1264 (2016)
Liu, P., Lyu, M., King, I., Xu, J.: Selflow: self-supervised learning of optical flow. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4571–4580 (2019)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Makinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)
Moghaddam, B., Yang, M.H.: Gender classification with support vector machines. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp. 306–311. IEEE (2000)
Ng, C.B., Tay, Y.H., Goi, B.-M.: Recognizing human gender in computer vision: a survey. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS (LNAI), vol. 7458, pp. 335–346. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32695-0_31
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623
Rai, P., Khanna, P.: Gender classification techniques: a review. In: Wyld, D.C., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications, pp. 51–59. Springer, Berlin Heidelberg (2012)
Ramachandra, R., Busch, C.: Presentation attack detection methods for face recognition systems: a comprehensive survey. ACM Comput. Surv. (CSUR) 50(1), 1–37 (2017)
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
Singh, A., Kumar, A., Jain, A.: Bayesian gait-based gender identification (BGGI) network on individuals wearing loosely fitted clothing. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 194–201. Springer, Heidelberg (2006). https://doi.org/10.1007/11760023_29
Szegedy, C., et al.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition (CVPR) (2015). http://arxiv.org/abs/1409.4842
Tammina, S.: Transfer learning using VGG-16 with deep convolutional neural network for classifying images. Int. J. Sci. Res. Publ. (IJSRP) 9, p9420, October 2019. https://doi.org/10.29322/IJSRP.9.10.2019.p9420
Sun, Z., Yuan, X., Bebis, G., Louis, S.J.: Neural-network-based gender classification using genetic search for eigen-feature selection. In: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN 2002 (Cat. No. 02CH37290), vol. 3, pp. 2433–2438 (2002)
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV 2005) Volume 1 - Volume 01, pp. 786–791. ICCV 2005. IEEE Computer Society, USA (2005). DOIurl10.1109/ICCV.2005.147. https://doi.org/10.1109/ICCV.2005.147
Zhang, Z., Hu, M., Wang, Y.: A survey of advances in biometric gait recognition. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) Biometric Recognition, pp. 150–158. Springer, Berlin Heidelberg, Berlin, Heidelberg (2011)
Zheng, J., Lu, B.L.: A support vector machine classifier with automatic confidence and its application to gender classification. Neurocomputer 74(11), 1926–1935, May 2011. https://doi.org/10.1016/j.neucom.2010.07.032. https://doi.org/10.1016/j.neucom.2010.07.032
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Singh, J.M., Ramachandra, R., Bours, P. (2021). Fusion of Texture and Optical Flow Using Convolutional Neural Networks for Gender Classification in Videos. In: Yildirim Yayilgan, S., Bajwa, I.S., Sanfilippo, F. (eds) Intelligent Technologies and Applications. INTAP 2020. Communications in Computer and Information Science, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-71711-7_19
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