Segment based co-factor detection and elimination for effective gait recognition

Matin, Abdul; Paul, Jyoti; Sayeed, Taufique

doi:10.1109/icivpr.2017.7890887

Cited by 4 publications

(2 citation statements)

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“…Also, given that viewing changes can prominently change the accessible visual features to be used, the recognition accuracy of GEI can go through a significant degradation if the viewing gaps get larger. In order to decline these negative influences, a wide range of machine learning methods have been raised for matching templates ( Kusakunniran et al, 2012a ; Matin, Paul & Sayeed, 2017 ; Kusakunniran et al, 2009 ). For example, in Kusakunniran et al (2009) , an adaptive weighting method was used to distinguish significance of bits for rescaled GEI.…”

Section: Related Workmentioning

confidence: 99%

“…In Kusakunniran et al (2012a) , a View Transformation Model (VTM) was proposed to learn the relationship between different views, and a view-invariant gait representation can be learned by projecting GEI into a latent subspace. In Matin, Paul & Sayeed (2017) , a method was proposed to detect co-factor affected segments of GEI. GEI is first divided into different parts based on the area of co-factor appearance.…”

Section: Related Workmentioning

confidence: 99%

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Robust clothing-independent gait recognition using hybrid part-based gait features

Gao¹,

Wu²,

Wang³

et al. 2022

PeerJ Computer Science

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Recently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once external factors (e.g., clothing variations) influence people’s gaits and changes happen in human appearances, a significant performance degradation occurs. Hence, in our article, a robust hybrid part-based spatio-temporal feature learning method is proposed for gait recognition to handle this cloth-changing problem. First, human bodies are segmented into the affected and non/less unaffected parts based on the anatomical studies. Then, a well-designed network is proposed in our method to formulate our required hybrid features from the non/less unaffected body parts. This network contains three sub-networks, aiming to generate features independently. Each sub-network emphasizes individual aspects of gait, hence an effective hybrid gait feature can be created through their concatenation. In addition, temporal information can be used as complement to enhance the recognition performance, a sub-network is specifically proposed to establish the temporal relationship between consecutive short-range frames. Also, since local features are more discriminative than global features in gait recognition, in this network a sub-network is specifically proposed to generate features of local refined differences. The effectiveness of our proposed method has been evaluated by experiments on the CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Related experiments illustrate that compared with other gait recognition methods, our proposed method can achieve a prominent result when handling this cloth-changing gait recognition problem.

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