2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

View-invariant gait recognition system using a gait energy image decomposition method

Gait recognition systems can capture biometrical information from a distance and without the user's active cooperation, making them suitable for surveillance environments. However, there are two challenges for gait recognition that need to be solved, namely when: (i) the walking direction is unknown and/or (ii) the subject's appearance changes significantly due to different clothes being worn or items being carried. This study discusses the problem of gait recognition in unconstrained environments and proposes a new system to tackle recognition when facing the two listed challenges. The system automatically identifies the walking direction using a perceptual hash (PHash) computed over the leg region of the gait energy image (GEI) and then compares it against the PHash values of different walking directions stored in the database. Robustness against appearance changes are obtained by decomposing the GEI into sections and selecting those sections unaltered by appearance changes for comparison against a database containing GEI sections for the identified walking direction. The proposed recognition method then recognises the user using a majority decision voting. The proposed view-invariant gait recognition system is computationally inexpensive and outperforms the state-of-the-art in terms of recognition performance.info:eu-repo/semantics/acceptedVersio

Gait recognition in the wild using shadow silhouettes

Gait recognition systems allow identification of users relying on features acquired from their body movement while walking. This paper discusses the main factors affecting the gait features that can be acquired from a 2D video sequence, proposing a taxonomy to classify them across four dimensions. It also explores the possibility of obtaining users’ gait features from the shadow silhouettes by proposing a novel gait recognition system. The system includes novel methods for: (i) shadow segmentation, (ii) walking direction identification, and (iii) shadow silhouette rectification. The shadow segmentation is performed by fitting a line through the feet positions of the user obtained from the gait texture image (GTI). The direction of the fitted line is then used to identify the walking direction of the user. Finally, the shadow silhouettes thus obtained are rectified to compensate for the distortions and deformations resulting from the acquisition setup, using the proposed four-point correspondence method. The paper additionally presents a new database, consisting of 21 users moving along two walking directions, to test the proposed gait recognition system. Results show that the performance of the proposed system is equivalent to that of the state-of-the-art in a constrained setting, but performing equivalently well in the wild, where most state-of-the-art methods fail. The results also highlight the advantages of using rectified shadow silhouettes over body silhouettes under certain conditions.info:eu-repo/semantics/acceptedVersio

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation