Investigation of robust gait recognition for different appearances and camera view angles

A gait recognition framework is proposed to tackle the challenge of unknown camera view angles as well as appearance changes in gait recognition. In the framework, camera view angles are firstly identified before gait recognition. Two compact images, gait energy image (GEI) and gait modified Gaussian image (GMGI), are used as the base gait feature images. Histogram of oriented gradients (HOG) is applied to the base gait feature images to generate feature descriptors, and then a final feature map after principal component analysis (PCA) operations on the descriptors are used to train support vector machine (SVM) models for individuals. A set of experiments are conducted on CASIA gait database B to investigate how appearance changes and unknown view angles affect the gait recognition accuracy under the proposed framework. The experimental results have shown that the framework is robust in dealing with unknown camera view angles, as well as appearance changes in gait recognition. In the unknown view angle testing, the recognition accuracy matches that of identical view angle testing in gait recognition. The proposed framework is specifically applicable in personal identification by gait in a small company/organization, where unintrusive personal identification is needed

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

View and clothing invariant gait recognition via 3D human semantic folding

A novel 3-dimensional (3D) human semantic folding is introduced to provide a robust and efficient gait recognition method which is invariant to camera view and clothing style. The proposed gait recognition method comprises three modules: (1) 3D body pose, shape and viewing data estimation network (3D-BPSVeNet); (2) gait semantic parameter folding model; and (3) gait semantic feature refining network. First, 3D-BPSVeNet is constructed based on a convolution gated recurrent unit (ConvGRU) to extract 2-dimensional (2D) to 3D body pose and shape semantic descriptors (2D-3D-BPSDs) from a sequence of gait parsed RGB images. A 3D gait model with virtual dressing is then constructed by morphing the template of 3D body model using the estimated 2D-3D-BPSDs and the recognized clothing styles. The more accurate 2D-3D-BPSDs without clothes are then obtained by using the silhouette similarity function when updating the 3D body model to fit the 2D gait. Second, the intrinsic 2D-3D-BPSDs without interference from clothes are encoded by sparse distributed representation (SDR) to gain the binary gait semantic image (SD-BGSI) in a topographical semantic space. By averaging the SD-BGSIs in a gait cycle, a gait semantic folding image (GSFI) is obtained to give a high-level representation of gait. Third, a gait semantic feature refining network is trained to refine the semantic feature extracted directly from GSFI using three types of prior knowledge, i.e., viewing angles, clothing styles and carrying condition. Experimental analyses on CMU MoBo, CASIA B, KY4D, OU-MVLP and OU-ISIR datasets show a significant performance gain in gait recognition in terms of accuracy and robustness

The utility of gait as a biological characteristic in forensic investigations – An empirical examination of movement pattern variation using biomechanical and anthropological principles

Forensic gait analysis is generally defined as the analysis of gait features from video footage to assist in criminal investigations. Although an attractive means to detect suspects since data can be collected from a distance without their knowledge, forensic gait analysis presently lacks method validation and quality standards, not only due to insufficient research, but also because certain scientific foundations, such as the assumption of gait uniqueness, have not been adequately addressed. To test the scientific basis of this premise, a suitable dataset replicating an ideal forensic gait analysis scenario was compiled from the Karlsruhe Institute of Technology (Germany) database. Biomechanical analysis of sagittal plane human motion in the bilateral shoulder, elbow, hip, knee, and ankle joints was conducted across complete gait cycles of twenty participants, to investigate the degree to which intraindividual variation impacts interindividual variation, according to the following aims: (1) to better understand the relationship between form (anatomy) and function (physiology) of human gait, (2) to investigate the basis of gait uniqueness by examining similarities and differences in joint angles, and (3) to build upon current theoretical foundations of gait-based human identification. The findings indicate different degrees of movement asymmetry given body region and gait sub-phase, thereby challenging previous methods employing interchangeable use of bilateral motion data, and the use of ‘average’ gait cycles to represent the gait of an individual irrespective of body side. Furthermore, interindividual variability in all five joints is influenced by body side to different extents depending on gait sub-phase and body region, thereby challenging the claim of holistic uniqueness of gait features across all body regions and gait events. Given the findings of this thesis and paucity regarding empirical basis to support expertise, exerting caution when evaluating gait-based evidence admissibility is highly recommended, since the utility of gait in identification is currently limited

Investigation of gait representations and partial body gait recognition

Recognising an individual by the way they walk is one of the most popular research subjects within the field of soft biometrics in last few decades. The advancement of technology and equipment such as Close Circuit Television (CCTV), wireless internet and wearable sensors makes it easier to obtain gait data than ever before. The gait biometric can be used widely and in different areas such as biomedical, forensic and surveillance. However, gait recognition still has many challenges and fundamental issues. All of these problems only serve as a researcher’s motivation to learn more about various gait topics to overcome the challenges and improve the field of gait recognition. Gait recognition currently has high performance when carried out under very specific conditions such as normal walking, obstruction from certain types of clothing and fixed camera view angles. When the aforementioned conditions are changed, the classification rate dramatically drops. This study aims to solve the problems of clothing, carrying objects and camera view angles within the indoor environment and video-based data collection. Two gait related databases used for testing in this study are CASIA dataset B and OU-ISIR Large population dataset with Bag (OU-LP-Bag). Three main tasks will be tested with CASIA dataset B while only gait recognition is tested with OU-LP-Bag. The gait recognition framework is developed to solve the three main tasks including gait recognition by identical view, view classification and cross view recognition. This framework uses gait images sequence as input to generate a gait compact image. Next, gait features are extracted with the optimal feature map by Principal Component Analysis (PCA) and then a linear Support Vector Machine (SVM) is used as the one-against-all multiclass classifier. Four gait compact images including Gait Energy Image (GEI), Gait Entropy Image (GEnI), Gait Gaussian Image (GGI) and the novel gait images called Gait Gaussian Entropy Image (GGEnI) are used as basic gait representations. Then three secondary gait representations are generated from these basic representations. These include Gradient Histogram Gait Image (GHGI) and two novel gait representations called Convolutional Gait Image (CGI) and Convolutional Gradient Histogram Gait Image (CGHGI). All representations are tested with three main tasks. When people walk, each body part does not have the same locomotion information, for example, there is much more motion in the leg than shoulder motion when walking. Moreover, clothing and carrying objects do not have the same level of affect to every part of the body, for example, a handbag does not generally affect leg motion. This study divides the human body into fourteen different body parts based on height. Body parts and gait representations are combined to solve the three main tasks. Three combined parts techniques which use two different parts to solve the problem are created. The fist is Part Scores Fusion (PSF) which uses the summation score of two models based on each part. The highest summation score model is chosen as the result. The second is Part Image Fusion (PIF) which concatenates two parts into a single image with a 1:1 ratio. The highest scoring model which is generated from image fusion is selected as the result. The third is Multi Region Duplication (MRD) which uses the same idea as PIF, however, the second part’s ratio is increased to 1:2, 1:3 and 1:4. These techniques are tested on the gait recognition by identical view. In conclusion, the general framework is effectively for three main tasks. GHGI-GEI which is generated from full silhouette is the most effective representation for gait recognition by identical view and cross view recognition. GHGI-GGI with lower knee region is the most effective representation for view angle classification. The GHGI-GEI CPI combination between full body and limb parts is the most effective combination on OU-LP-Bag. A more detailed description of each aspect is in the following Chapters