Hau-Lee Tong

Extraction of human gait features from enhanced human silhouette images

In this paper, a new approach is proposed for extracting human gait features from a walking human based on the silhouette image. The approach consists of five stages: clearing the background noise of image by morphological opening; measuring the width and height of the human silhouette; dividing the enhanced human silhouette into six body segments based on anatomical knowledge; applying morphological skeleton to obtain the body skeleton; and applying Hough transform to obtain the joint angles from the body segment skeletons. The joint angles together with the height and width of the human silhouette are collected and used for gait analysis. From the experiment conducted, it can be observed that the proposed system is feasible as satisfactory results have been achieved.

MMU GASPFA: A COTS multimodal biometric database

This paper describes the baseline corpus of a new multimodal biometric database, the MMU GASPFA (Gait-Speech-Face) database. The corpus in GASPFA is acquired using commercial off the shelf (COTS) equipment including digital video cameras, digital voice recorder, digital camera, Kinect camera and accelerometer equipped smart phones. The corpus consists of frontal face images from the digital camera, speech utterances recorded using the digital voice recorder, gait videos with their associated data recorded using both the digital video cameras and Kinect camera simultaneously as well as accelerometer readings from the smart phones. A total of 82 participants had their biometric data recorded. MMU GASPFA is able to support both multimodal biometric authentication as well as gait action recognition. This paper describes the acquisition setup and protocols used in MMU GASPFA, as well as the content of the corpus. Baseline results from a subset of the participants are presented for validation purposes.

Classification of human gait features with different apparel and walking speed

In this paper, we proposed a new approach for the classification of human gait features with different apparel and various walking speed. The approach consists of two parts: extraction of human gait features from enhanced human silhouette and classification of the extracted human gait features using fuzzy k-nearest neighbours (KNN). The joint angles together with the height, width and crotch height of the human silhouette are collected and used for gait analysis. The training and the testing sets are separable without overlapping. Both sets involve nine different apparel and three walking speed. From the experiment conducted, it can be observed that the proposed system is feasible as satisfactory results have been achieved.

Extraction and Classification of Human Gait Features

In this paper, a new approach is proposed for extracting human gait features from a walking human based on the silhouette images. The approach consists of six stages: clearing the background noise of image by morphological opening; measuring of the width and height of the human silhouette; dividing the enhanced human silhouette into six body segments based on anatomical knowledge; applying morphological skeleton to obtain the body skeleton; applying Hough transform to obtain the joint angles from the body segment skeletons; and measuring the distance between the bottom of right leg and left leg from the body segment skeletons. The angles of joints, step-size together with the height and width of the human silhouette are collected and used for gait analysis. The experimental results have demonstrated that the proposed system is feasible and achieved satisfactory results.

Automated hemorrhage slices detection for CT brain images

This paper presents an automated method to detect the hemorrhage slices for Computed Tomography(CT) brain images. The proposed system can be divided into two stages which are preprocessing stage and detection stage. Preprocessing basically is to prepare and enhance the images for the detection stage. During detection stage, new midline detection approach is proposed to divide the intracranial area into left and right hemispheres. Then histogram features are extracted from left and right hemispheres for the dissimilarity comparison. All the feature components will be channeled into support vector machine (SVM) classifier to determine the existence of the hemorrhage. Tenfold cross validation was applied during the SVM classification. The experiments were performed on 450 CT images and results were evaluated in terms of recall and precision. The recall and precision obtained from experimental results for hemorrhage slices detection are 84.86% and 96.82% respectively.

Development of Vision Based Multiview Gait Recognition System with MMUGait Database

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Improved gait recognition with automatic body joint identification

Gait recognition is an unobtrusive biometric, which allows identification of people from a distance by the manner in which they walk. In this paper, a new approach is proposed for extracting human gait features based on body joint identification from human silhouette images. In the proposed approach, the human silhouette image is first enhanced to remove the artifacts before it is divided into eight segments according to a priori knowledge of human body proportion. Next, the body joints which act as the pivot points in human gait are automatically identified and the joint trajectories are computed. To assess the performance of the extracted gait features, fuzzy k-nearest neighbor classification technique is used to identify subjects from the SOTON covariate database. The experimental results have shown that the gait features extracted using the proposed approach are effective as the recognition rate has been improved.

Gait classification with different covariate factors

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime. This paper proposes a new approach for gait classification with twelve different covariate factors. The proposed approach is consisted of two parts: extraction of human gait features from enhanced human silhouette and classification of the extracted human gait features using fuzzy k-nearest neighbours (KNN). The joint trajectories together with the height, width and crotch height of the human silhouette are collected and used for gait analysis. To improve the recognition rate, two of these features are smoothened before the classification process in order to alleviate the effect of outliers. Experimental results of a dataset involving nine walking subjects have demonstrated the effectiveness of the proposed approach.

Gait Classification by Support Vector Machine

This paper presents a simple model-free gait extraction approach for human identification by using Support Vector Machine. The proposed approach consists of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by Support Vector Machine (SVM). The gait features extracted are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the classification performance, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on SOTON covariate database, which is comprised of eleven subjects walking bidirectional in a controlled indoor environment with thirteen different covariate factors that vary in terms of apparel, walking speed, shoe types and carrying objects. From the experimental results, it can be concluded that the proposed approach is effective in human identification from a distance.

Comparison of Linear Discriminant Analysis and Support Vector Machine in Classification of Subdural and Extradural Hemorrhages

This paper describes new features for the classification of different types of extra-axial intracranial hemorrhages namely subdural hemorrhage(SDH) and extradural hemorrhage(EDH) on brain computed tomography(CT) scans. The main objective is to create an automatic retrieval system to reduce the time spent searching manually for the hemorrhagic images. Besides, the challenge is to locate suitable features to differentiate the SDH and EDH. One of the methods to distinguish EDH and SDH is through their shapes. Thus, a shape-based feature extraction is proposed in order to differentiate the SDH and EDH. For the classification part, we present a comparative study of linear discriminant analysis(LDA) and support vector machine(SVM) with linear kernal for the classification of SDH, EDH and normal regions. Both pattern classification techniques map pattern vectors to a high dimensional feature space to construct the optimal margin separating hyperplane. To conclude, SVM outperforms LDA from the obtained classification results.