Analysis of Histogram of Oriented Gradients on Gait Recognition

Abstract

This study investigates the impact of Histogram of Oriented Gradients (HoG) on gait recognition. HoG is applied to four basic gait representations, i.e. Gait Energy Image (GEI), Gait Entropy Image (GEnI), Gait Gaussian Image (GGI), and newly developed Gait Gaussian Entropy Image (GGEnI). Hence their corresponding secondary gait representations, Gradient Histogram Gait Images (GHGI), are generated. Due to the nature of HoGs, the secondary gait representations contain rich information of images from different scales and orientations. The optimized HoG parameters are investigated to establish appropriate parameter settings in the HoG operations. Evaluations are conducted by using Support Vector Machines (SVM) as classifier swith CASIA dataset B. Experimental results have shown that HoG associated secondary representations are superior to the originally basic representations in gait recognition, especially in case of coping with appearance changes, such as walking with bag and walking with coat when using normal walking samples in training. GHGIs have increased the gait recognition rate approximately of 17% to GEI, 12% to GEnI, 24% to GGI and 20% to GGEnI.

Machine learning models and methods for human gait recognition

“…Paper Features [18,19] The dimensions of various body components in terms of length and height [20,21], [22] The widths of silhouette rows [23] The Fourier descriptors pertaining to the silhouette outline [24] The average silhouette of gait energy image (GEI) [25,26] Energy expenditure associated with human gait [27] Image representation that captures the distribution and intensity of gradients within an image [28] Gait entropy [29] A histogram depicting the distribution of normal vectors along the silhouette outline Source: compiled by the authors…”

“…Several commonly used methods in the field of computer vision for background subtraction include frame differencing [26], background estimation by averaging 𝑛 frames [27], and Gaussian average computation [28], among others.…”