The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
A Study on Gait-Based Gender Classification
Abstract
Gender is an important cue in social activities. In this correspondence, we present a study and analysis of gender classification based on human gait. Psychological experiments were carried out. These experiments showed that humans can recognize gender based on gait information, and that contributions of different body components vary. The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy. The proposed method which combines human knowledge achieves higher performance than some other methods, and is even more accurate than human observers. We also present a numerical analysis of the contributions of different human components, which shows that head and hair, back, chest and thigh are more discriminative than other components. We also did challenging cross-race experiments that used Asian gait data to classify the gender of Europeans, and vice versa. Encouraging results were obtained. All the above prove that gait-based gender classification is feasible in controlled environments. In real applications, it still suffers from many difficulties, such as view variation, clothing and shoes changes, or carrying objects. We analyze the difficulties and suggest some possible solutions.
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Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
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The recognition of human movement using temporal templates
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Pattern Classification (2nd ed.)
Duda, R. O., Hart, P. E., & Stork, D. G. (1999) · Cited by 2,520
Individual recognition using gait energy image
Han, J., & Bhanu, B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.38 · Cited by 1,847
Silhouette analysis-based gait recognition for human identification
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Self-citationGeneral Tensor Discriminant Analysis and Gabor Features for Gait Recognition
Tao, D., Li, X., Wu, X., & Maybank, S. J. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1096 · Cited by 1,140
Self-citationThe humanID gait challenge problem: data sets, performance, and analysis
Sarkar, S., Phillips, P. J., Liu, Z., Vega, I. R., Grother, P., & Bowyer, K. W. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.39 · Cited by 1,133
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Cited by
(2019) · ACM Computing Surveys · doi:10.1145/3230633 · Cited by 303
A hybrid deep learning CNN–ELM for age and gender classification
Duan, M., Li, K., Yang, C., & Li, K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2017.08.062 · Cited by 258
Metadata
- Authors
- Yu, S., Tan, T., Huang, K., Jia, K., & Wu, X.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2009-08
- Volume
- 18
- Issue
- 8
- Pages
- 1905-1910
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2152914237
- Cited by
- 293
- References
- 30
- FWCI
- 11.45
- Type
- journal article
- Language
- en
- Open access
- closed
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