Human Gait Recognition Based on Principal Component Analysis - Das, Pranjit; Saharia, Sarat (International Journal of Computer Sciences and Engineering, 2025)
This paper represents a method to recognize the human walking individuals by their gait using Principal Component Analysis (PCA). Human Gait is used as principal identifying feature to generate the unique gait sequence for each individual. Gait is an important biometric feature which delineates the way of locomotion. The generation of binary silhouette frames of walking subjects is the initial step in this method. Some distinguishable gait features, viz., centroid, aspect ratio, orientation, height and width are extracted from the silhouette frames to acquire feature vectors. Then, the PCA is employed over the generated feature vectors to condense the information contained and produces the principal components which are used as gait sequences or signatures to represent each walking individuals. Finally the generated gait sequences are recognized by using a minimum distance classifier based on eluclidean distance. A classification rate of 93% is achieved from the proposed human recognition method which is tested using CASIA dataset.