Abstract
Person recognition from gait images is not robust to speed changes. To deal with this problem, generally existing methods have focused on training a model to transform gait features from various speeds into a common walking speed, and the model was trained with gait images with a variety of speeds. However in case that a subject walks with a speed which is not trained in the model, the performance gets worse. In this paper we introduce an idea that an image set-based matching approach, which omits walking speed information, has a potential to solve the problem. This is based on the assumption that speed information may not be critical information to gait recognition, since speed variations are universal phenomena. To prove the proposed idea, we apply a mutual subspace method to gait images and show the effectiveness of the proposed idea with the OU-ISIR gait speed transition database.
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Han, J., Bhanu, B.: Individual Recognition using Gait Energy Image. IEEE Trans. on Pattern Analysis and Machine Intelligence, 28(2) (2006)
Zhang, E., Zhao, Y., Xiong, W.: Active energy image plus 2DLPP for gait recognition. Signal Processing 90(7), 2295–2302 (2010)
Shinzaki, M., Iwashita, Y., Kurazume, R., Ogawara, K.: Gait-based person identification method using shadow biometrics for robustness to changes in the walking direction. In: IEEE Winter Conf. on Applications of Computer Vision, pp. 670–677 (2015)
Iwashita, Y., Baba, R., Ogawara, K. Kurazume, R.: Person identification from spatio-temporal 3D gait. In: Int. Conf. Emerging Security Technologies (EST), pp. 30–35 (2010)
Iwashita, Y., Ogawara, K., Kurazume, R.: Identification of people walking along curved trajectories. Pattern Recognition Letters 46, 60–69 (2014)
CASIA Gait Database. http://www.sinobiometrics.com
Iwama, H., Okumura, M., Makihara, Y., Yagi, Y.: The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition. IEEE Trans. on Information Forensics and Security 7(5), 1511–1521 (2012)
Tanawongsuwan, R., Bobick, A.: Modelling the effects of walking speed on appearance-based gait recognition. Computer Vision and Pattern Recognition (2004)
Liu, Z., Sarkar, S.: Improved gait recognition by gait dynamics normalization. IEEE Trans. on Pattern Analysis and Machine Intelligence 28 (2006)
Mansur, A., Makihara, Y., Aqmar, R., Yagi, Y.: Gait Recognition under Speed Transition. In: IPSJ Trans. on Computer Vision and Applications, Computer Vision and Pattern Recognition (2014)
Liu, N., Lu, J., Tan, Y., Li, M.: Set-to-set gait recognition across varying views and walking conditions. In: IEEE Int. Conf. on Multimedia and Expo, pp. 1–6 (2011)
Connie, T., Go, M., Teoh, A.: A Grassmann graph embedding framework for gait analysis. EURASIP Journal on Advances in Signal Processing (2014)
Sakano, H., Mukawa, N.; Kernel mutual subspace method for robust facial image recognition. In: Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 245–248 (2000)
Maeda, K., Watanabe, S.: Pattern matching method with local structure. Trans. on IEICE (D) (Japanese Edition) 68–D(3), 345–352 (1985)
Maeda, K.: From the Subspace Methods to the Mutual Subspace Method. Computer Vision 285, 135–156 (2010)
Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: IEEE Int. Conf. on Face and Gesture Recognition, pp. 318–323 (1998)
Chatelin, F.: Veleurs propres de matrices. Masson (1988) (In French)
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Iwashita, Y., Sakano, H., Kurazume, R. (2015). Gait Recognition Robust to Speed Transition Using Mutual Subspace Method. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_13
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DOI: https://doi.org/10.1007/978-3-319-23231-7_13
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