Gait-based gender classification using neutral and non-neutral gait sequences

A biometric system provides automatic identification of an individual based on his/ her unique feature or characteristic. Biometric identifiers are often categorized as physiological versus behavioural characteristics. Physiological characteristics are related to the shape of the body, like fingerprint, palm veins, face shape, while behavioural characteristics are related to the pattern of an individual‟s behaviour, including gait, handwritten signature and voice. Gait as a means of biometric recognition aims to recognize a person by he/she walks. Human gait feature could be used in different applications such as identifying unauthorized persons, identifying their gender, and determining walking-related abnormalities by analysing the way they walk or move. In this thesis we aim to propose gender classification based on human gait features to investigate the problem of non-neutral gait sequences: coat wearing and carrying bag conditions in addition to the neutral gait sequences. Our objectives will focus on investigating and testing the performance of gait sequence features for the purpose of gender classification. Our tests are based on large number of experiments using CASIA B gait database, includes 124 subjects (31 women and 93 men), recorded from 11 different view angles. For each subject, there are 10 walking sequences, consisting of 6 Neutral sequences (Nu), 2 Bag-Carrying sequences (CB) and 2 Coat-Wearing sequences (CW). The proposed method mainly classified into three parts; the first part is focused on investigation of isolating conductive frames from their backgrounds using the frame differencing method. The second part is related to feature extraction, for which we propose a new set of features which are constructed as based on the Gait Energy Image and Gait Entropy Image, called Gait Entropy Energy Image (GEnEI). Three different feature sets are structured from GEnEI based Wavelet Transform, called Approximation coefficient Gait Entropy Energy Image (AGEnEI), Vertical coefficient Gait Entropy Energy Image (VGEnEI), and Approximation and Vertical coefficients Gait Entropy Energy Image (AVGEnEI). Finally two different classification methods are applied to test the performance of the proposed methods separately, called k-Nearest-Neighbour (k-NN) and Support Vector Machine (SVM). Further, these three sets of features are tested separately using the fused-based decision level fusion method. We demonstrate that when k-NN is used as a classification method, AGEnEI results in 97% fusion level for Nu gait sequence, VGEnEI results in 91.4% fusion level for CB sequence and for CW sequence AGEnEI produces 83.6% fusion level. Among three sets of features, k=1 notably produces better average fusion level compared to the other two sets of features, i.e. k=3 and k=5. When three sets of features (AGEnEI, VGEnEI , AVGEnEI ) are fused using the decision level fusion method, we obtain accuracy of 99.8%, 92.2% and 86.3% for Nu, CB and CW respectively. These results outperform the results achieved when each of these sets of features are applied separately.