An Enhanced Human Gait Recognition System using UALNN Classifier

Abstract

Human gait recognition is a biometric identification method that uses a person’s gait patterns to identify them. Gait Recognition (GR) is the steps to follow in recognizing walking style of an individual. It identifies a person as of a longer distance with higher precision owing to its unique characteristics; thus, it is mostly utilized for tracking purposes. In computer vision software, specifically, to track the same individual across multiple scenarios, the individual recognition across numerous cameras is a significant task. However, owing to the position of caught image or video, human identification by means of GR could be limited. Therefore, for person re-identification, a novel GR methodology has been proposed here. Five phases were undergone by the proposed methodology. Initially, from an openly accessible dataset, the input video is collected, which is then transmuted into a number of frames. To eliminate the noise from the frames, the Mean Modified Filter (MMF) is utilized after frame conversion. The Gaussian Taxicab Mixture Model (GTMM) is employed to get rid of the background from the frames. After that, Similarity Histogram Silhouette Segmentation (SHSS) model is utilized to segment the humans’ silhouette as of the background removed frames; thus, generating the energy image. The features are extracted from this energy image. The Uniform Alex Neural Network classifier (UALNN) uses these features to identify the human gait. Improved recognition performance is attained from the experiential outcomes. The overall recognition outcomes obtained concluded that the proposed methodology is highly promising.

KEYWORDS:

• Gaussian Taxicab Mixture Model (GTMM)
• Mean Modified Filter (MMF)

Disclosure statement

No potential conflict of interest was reported by the author(s).

DATA AVAILABILITY STATEMENT

Since no new data were generated or examined for this study, data sharing is not applicable to this article.

Additional information

Notes on contributors

K.T. Thomas

KT Thomas graduated with an MCA degree in 2000 from Madras University and a master of engineering in computer science and engineering (ME(CSE)) in 2014 from Anna University in Chennai, India. Currently a research scholar at the School of Computer Sciences, Mahatma Gandhi University, Kottayam, India. Corresponding author. Email: [email protected]

K.P. Pushpalatha

KP Pushpalatha received MCA from Madurai Kamaraj University, Madurai, India and PhD from Mahatma Gandhi University, Kottayam, India. Currently working as professor and head of School of Computer Sciences, Mahatma Gandhi University, Kottayam. Also serves as chairman, board of studies, PG School of Computer Sciences, Mahatma Gandhi University, Kottayam. Email: [email protected]