On Exploring the Role of Feature Processing in Gait-based Gender Identification

The advent of Internet of Things (IoT) in all domains of human life has made life smarter and simpler. The augmentation of machine intelligence in solving research problems focused on decision-making, has enriched the outcome of such research. In the field of biometric identification, IoT based research has seen rapid progress. Apart from the conventional biometric features, such as iris, finger-tips, etc., the possibility of using gait features from human walking activity has also been explored as a potential biometric feature. In the proposed work, a standard dataset gathered using heterogeneous sensors from Male and Female volunteers has been used. The experiments explore how the feature-scaling and Principal Component Analysis (PCA) methods enable the development of a fool-proof automated system based on Multi-Layer Perceptron (MLP), for identifying Male and Female gender based on gait signature.