Abstract
Gait has emerged as a new biometric verification method which helps in recognising a person by his walking style. In this paper, gait features are extracted based on information set theory, which itself is derived from fuzzy set theory. The uncertainty in the information source values is taken into account by entropy function, based on which gait information image (GII) is derived from a gait cycle. For this purpose a new GII based feature named bipolar sigmoid feature (GII-BPSF) is proposed. Moreover, to address the problem of orientation normalization for different view angles, a modified pre-processing method is adapted from the study of He et al. (The role of size normalization on the recognition rate of handwritten numerals, 2005) to verify the robustness of the proposed features, experiments were carried out on CASIA (Institute of Automation, Chinese Academy of Sciences) dataset B with a wide range of subject variation, different clothing patterns, and carrying conditions. The experimental results show that the proposed GII-BPSF is a more efficient gait representation and feature for an individual recognition and the obtained identification rates are higher concerning the previously established gait recognition approaches.
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Notes
A rank n is given to a test case when it is nth closest match corresponding to its trained sample in the classifier used. For example, rank 5 depicts that the trained sample corresponding to its test case lies in top 5 matches. In our study, the maximum rank can be the 124 i.e. the total number of subjects. In Fig. 5, the maximum rank achieved is less than 60 which itself proves the robustness of GII-BPSF i.e. every test case can be found under at-most 60 rank, and the identification rate is understood to be 100% for all ranks above 60 till 124.
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Sharma, H., Grover, J. Human identification based on gait recognition for multiple view angles. Int J Intell Robot Appl 2, 372–380 (2018). https://doi.org/10.1007/s41315-018-0061-y
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DOI: https://doi.org/10.1007/s41315-018-0061-y