Features Fusion-Based Gait Recognition with Covariate Conditions

Abstract

Identification of a person based on their walking pattern is affected by factors such as camera viewing angle, apparel with the subject, holding a bag, walking surface, and complex situations. These are the covariate conditions in human gait analysis. The covariate conditions change an individual's gait pattern, making it difficult to implement gait recognition in a realistic environment. This work addresses the issue of covariate conditions in gait identification through a fusion of features. Using a pre-trained VGG16 model with four fully connected layers, the dynamic features are extracted and merged with HoG (Histogram of Oriented Gradients) features extracted from the raw GEI (Gait Energy Image) gait templates. PCA (Principal Component Analysis) is then used to lower the dimension of the combined features in order to select the discriminant feature vectors. The CASIA-B dataset is used to examine the efficacy of the suggested technique, which employs an MLP (Multi-layer Perceptron) classifier. The findings show that the proposed technique outperforms other existing approaches in terms of accuracy while walking normally, wearing a coat, and carrying a bag under identical viewing conditions.