CSA  >> Vol. 7 No. 4 (April 2017)

    Gait Recognition Algorithm Based on Sparse Representation of Joint Multi-Feature Dictionary

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胡 欣,吴晓红,雷 翔,何小海:四川大学电子信息学院,四川 成都

步态识别联合稀疏RASGait Recognition Joint Sparse RAS



Most of the existing gait recognition algorithms extract the single feature using model features or global features. However, these algorithms usually have a poor robustness and a low recognition rate in practical situations such as multi-angle. To solve this problem, a gait recognition algorithm based on joint sparse representation of multi-feature dictionaries is proposed in this paper. In this algorithm, three characteristics in different particle size are selected: Procrustes Mean Shape, Gait Energy Image and Region Area Sequence which is structured in this article. Feature training dictionaries are constructed and a multidisciplinary sparse representation to feature samples is done. Finally, the test sample category is obtained by calculating the minimum cumulative residual and achieves the integration of feature layer. Experimental results show that the multi-feature joint recognition method used in this paper has a higher recognition rate and a certain robustness at multiple angles compared to single feature extraction and recognition. This paper basically fulfills the complementary information between features.

胡欣, 吴晓红, 雷翔, 何小海. 基于联合多特征字典稀疏表示的步态识别算法[J]. 计算机科学与应用, 2017, 7(4): 398-406. https://doi.org/10.12677/CSA.2017.74048


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