![]() ![]() Makihara Y, Sagawa R, Mukaigawa Y, Echigo T, Yagi Y (2006) Which reference view is effective for gait identification using a view transformation model. IEEE Trans Pattern Anal Mach Intell 24(4):442–455 Lanitis A, Taylor C, Cootes T (2002) Toward automatic simulation of aging effects on face images. In Proceedings of IEEE International Conference on Advanced Video and Signal Based Surveillance, pp 143–150 Kale A, Ro圜howdhury AK, Chellappa R (2003) Towards a view invariant gait recognition algorithm. In Proceedings of International Conference on Pattern Recognition, pp 1–4 Jean F, Bergevin R, Albu AB (2008) Trajectories normalization for viewpoint invariant gait recognition. Huber P, Ronchetti EM (2009) Robust statistics, 2nd edn. Huber PJ (1964) Robust estimation of a location parameter. In Proceedings of IEEE International Conference on Image Processing, 1:297–300 Han J, Bhanu B, Ro圜howdhury AK (2005) A study on view insensitive gait recognition. In Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp 1–6 Goffredo M, Seely RD, Carter JN, Nixon MS (2008) Markerless view independent gait analysis with self-camera calibration. Am J Anat 120(1):33–54įukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. In Proceedings of International Conference on Audio- and Video-based Biometric Person Authentication, pp 43–48ĭempster WT, Gaughran GRL (1967) Properties of body segments based on size and weight. In International Conference on Biometrics, pp 990–999Ĭunado D, Nash JM, Nixon MS, Carter JN (1999) Gait extraction and description by evidence-gathering. In Proceedings of International Conference on Audio- and Video-based Biometric Person Authentication, pp 284–294īouchrika I, Goffredo M, Carter JN, Nixon MS (2009) Covariate analysis for view-point independent gait recognition. This point is verified experimentally through integrating the view angle estimation into a gait based gender classification system.īenAbdelkader C, Cutler R, Nanda H, Davis L (2001) Eigengait: motion-based recognition of people using image self-similarity. Therefore, it can provide necessary help for gait application systems when the view angles of test data are uncertain. Compared with the ground truth angles, such estimation is satisfactory with a small error level. The view angles of test samples from BUAA-IRIP Gait Database are estimated with the regression models learned from CASIA Gait Database. Afterwards, the robust regression method is employed to estimate the viewpoint of gait. The discrimination power of this representation is also verified through experiments. We propose a novel and effective feature extraction method to characterize the silhouettes from different views. In order to obtain reliable estimation results, the view-sensitive features should be extracted. In this paper, we present an idea of estimating the view angle of a test sample in advance so as to compare it with the corresponding training samples with the same or approximate viewpoint. The performance of most gait recognition methods would drop down if the viewpoint of test data is different from the viewpoint of training data.
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