Clothing invariant human gait recognition using modified local optimal oriented pattern binary descriptor

Abstract

Human gait is a behavioral characteristic which has received a large amount of consideration in recent times as a biometric identifier. The clothing variance is one of the most common covariate influences which can influence the performance of gait recognition approach in real-world scenarios. This paper proposes a gait recognition approach proficient in choosing information characteristics for individual identification under different clothing conditions. The proposed method constitutes of addressing the feature extraction technique by introducing a binary descriptor called as Modified Local Optimal Oriented Pattern (MLOOP). In the proposed approach, initially, the feature vectors such as histogram and horizontal width vector are extracted from MLOOP descriptor, and then the dimensionality of the feature vector is reduced to remove the irrelevant features. The performance of MLOOP was accessed against its predecessors. Obtained experimental results demonstrate that the MLOOP descriptor performs better than the previous binary descriptors. Furthermore, the performance analysis of the proposed approach was assessed on OU-ISIR B treadmill gait database and CASIA B gait database. Broad investigations demonstrate the viability of the proposed technique. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.