Person recognition using gait energy imaging

Lishani, Ait (2018) Person recognition using gait energy imaging. Doctoral thesis, Northumbria University.

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Abstract

Biometric technology has emerged as a viable identification and authentication solution with various systems in operation worldwide. The technology uses various modalities, including fingerprint, face, iris, palmprint, speech, and gait. Biometric recognition often involves images or videos and other image impressions that are fragile and include subtle details that are difficult to see or capture. Thus, there is a need for developing imaging applications that allow for accurate feature extraction from images for identification and recognition purposes.

Biometric modalities can be classified into two classes: physiological (i.e. fingerprint, iris, face, palm-print) or behavioural traits (speech, gait). This work is concerned with an investigation of biometric recognition at a distance and the gait modality has been chosen for various reasons. Gait data can be captured at a distance and is non-invasive. Additionally, it has advantages such as the fact that a person’s gait is hard to copy, and by trying to do so, the imitator will likely appear more suspicious. Although, due to covariates, for example, a change in viewing angle, clothes, shoes, shadow or elapsed time can make gait recognition additionally challenging. There are several approaches for studying gait recognition systems such as model-based and model-free. This thesis is based on a model-free approach and proposes a supervised feature extraction approach capable of selecting distinctive features for the recognition of human gait under clothing and carrying conditions.

In this work; to allow for the characterisation of human gait properties for individual recognition, a spatiotemporal gait representation technique called Gait Energy Image (GEI) has been used. This approach is aimed at improving the recognition performance based on the principles of feature texture descriptors extracted from GEI. Furthermore, as part of this work, the dynamic parts of the energy gait representation have been proposed as means to extract more discriminative information from a gait sequence using reduction techniques in order to further improve the human identification rate.

The four methods proposed were evaluated using CASIA Gait Database (dataset B) and USF Database under variations of clothing and carrying conditions for different viewing angles.

The first method is based on Haralick texture feature, and use the RELIEF selection algorithm. This method showed that a judicious deployment of horizontal GEI features outperforms similar methods by up to 7.00%. In addition, this method achieved an improved classification rate of up to 80.00% from a side view of 90o.

The second and third contributions are concerned with an investigation of the Gabor filter bank and Multi-scale Local Binary Pattern (MLBP) as an efficient feature extraction for gait recognition under clothing distortions. To achieve this, various dimension reduction techniques including Kernel Principal Component Analysis, Maximum Margin Projection, Spectral Regression Kernel Discriminant Analysis and Locality Preserving Projections were investigated. The results showed that the proposed methods outperform the state-of-the-art counterparts by achieving up to 93.00% Identification Rate (IR) at rank-1 using the Gabor filter method, and achieving up to 92.00% IR using the MLBP method, when using a k-NN classifier for a side view of 90o.

The final contribution of this work is concerned with an investigation of the Haar wavelet transform and its use for extracting powerful features for human gait recognition under clothing distortions. The experimental results using a k-NN classifier yielded attractive results of up to 93.00% in terms of highest IR at rank-1, compared to existing and similar state-of-the-art methods. It should be noted that all the experiments were carried out using the MATLAB programming environment.

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