A Person Identification Method Using a Top-View Head Image from an Overhead Camera
Ryota Nakatani, Daichi Kouno, Kazutaka Shimada, and Tsutomu Endo
Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
In this paper, we describe a novel image-based person identification task. Conventional face-based person identification methods have a low tolerance for occluded situations, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces restrictions on the installation location of the camera and solves the problem of occluded images. First, our method identifies a person area in a captured image by using background subtraction. Then, it extracts four features from the area: (1) body size, (2) hair color, (3) hairstyle, and (4) hair whorl. We apply the four features to the AdaBoost algorithm. Experimental results show the effectiveness of our method.
-  T. Kanade, “Picture processing by computer complex and recognition of human face,” Technical report, Kyoto University, Dept. of Information Science, 1973.
-  S.Watanabe and N. Pakvasa, “Subspace method in pattern recognition,” in Proc. of 1st Int. J. Conf on Pattern Recognition, pp. 2-32, 1973.
-  M. Turk and A. P. Pentland, “Eigenfaces for recognition,” J. of Cognitive Neuroscience, Vol.3, No.1, pp. 71-86, 1991.
-  H. Uchida, R. Inoda, T. Tsuji, and S. Abe, “Counting people and recognizing wheelchairs at elevator lobby by real-time image processing,” IEEJ Trans. on Industrial Applications, Vol.129, No.6, pp. 578-584, 2009.
-  M. Onishi and I. Yoda, “Visualization of Customer Flow in an Office Complex over a Long Period,” Proc. of Int. Conf. on Pattern Recognition (ICPR), pp. 1747-1750, 2010.
-  Y. Iwashita and A. Stoica, “Gait Recognition using Shadow Analysis,” Proc. of Symposium on Bio-inspired, Learning, and Intelligent Systems for Security 2009, pp. 26-31, 2009.
-  T. Fukuzoe, M. Itou, D. Mito, and M. Watanabe, “A person identification method unifying image features of body shape and daily habit based on probabilistic recognition framework,” IEICE Trans. on Information and Systems, D, Vol.91-D, No.5, pp. 1369-1379, 2008.
-  N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 886-893, 2005.
-  Y. Freund and R. E. Schapier, “Experiments with a new boosting algorithm,” Proc. of ICML, pp. 148-156, 1996.
-  J. R. Quinlan, “C4.5 Programs for Machine Learning,” Morgan Kaufmann Publishers, 1993.
-  A. C. Gallagher and T. Chen, “Using Context to Recognize People in Consumer Images,” IPSJ Trans. on Computer Vision and Applications, Vol.1, pp. 115-126, 2009.
-  J. Yamaguchi, K. Shimada, S. Enokida, T. Ejima, and T. Endo, “Personal Identification Using Facial Feature and Context,” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.23, No.2, pp. 13-21, 2011.
-  M. Farenzena, L. Bazzani, A. Perina, V. Murino, and M. Cristani, “Person re-identification by symmetry-driven accumulation of local features,” Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2360-2367, 2010.
-  D. Kouno, K. Shimada, and T. Endo, “Person Identification Using Top-view Image with Depth Information,” Proc. of 13th ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2012), 2012.