Gait recognition using normalized shadows

WOS:000426986000189 (Nº de Acesso Web of Science)Surveillance of public spaces is often conducted with the help of cameras placed at elevated positions. Recently, drones with high resolution cameras have made it possible to perform overhead surveillance of critical spaces. However, images obtained in these conditions may not contain enough body features to allow conventional biometric recognition. This paper introduces a novel gait recognition system which uses the shadows cast by users, when available. It includes two main contributions: (i) a method for shadow segmentation, which analyzes the orientation of the silhouette contour to identify the feet position along time, in order to separate the body and shadow silhouettes connected at such positions; (ii) a method that normalizes the segmented shadow silhouettes, by applying a transformation derived from optimizing the low rank textures of a gait texture image, to compensate for changes in view and shadow orientation. The normalized shadow silhouettes can then undergo a gait recognition algorithm, which in this paper relies on the computation of a gait energy image, combined with linear discriminant analysis for user recognition. The proposed system outperforms the available state-of-the-art, being robust to changes in acquisition viewpoints.info:eu-repo/semantics/acceptedVersio

View-invariant gait recognition exploiting spatio-temporal information and a dissimilarity metric

In gait recognition, when subjects do not follow a known walking trajectory, the comparison against a database may be rendered impossible. Some proposed solutions rely on learning and mapping the appearance of silhouettes along various views, with some limitations caused for instance by appearance changes (e.g. coats or bags). The present paper discusses this problem and proposes a novel solution for automatic viewing angle identification, using minimal information computed from the walking person silhouettes, while being robust against appearance changes. The proposed method is more efficient and provides improved results when compared to the available alternatives. Moreover, unlike most state-of-the- art methods, it does not require a training stage. The paper also discusses the use of a dissimilarity metric for the recognition stage. Dissimilarity metrics have shown interesting results in several recognition systems. This paper also attests the strength of a dissimilarity-based approach for gait recognition.info:eu-repo/semantics/acceptedVersio

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

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

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