It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in gait-based application methods because they significantly change a person's appearance. This paper proposes a novel method for classifying human gender in real time using gait information. The use of an average gait image (AGI), rather than a gait energy image (GEI), allows this method to be computationally efficient and robust against view changes. A viewpoint (VP) model is created for automatically determining the viewing angle during the testing phase. A distance signal (DS) model is constructed to remove any areas with an attachment (carried items, worn coats) from a silhouette to reduce the interference in the resulting classification. Finally, the human gender is classified using multiple view-dependent classifiers trained using a support vector machine. Experiment results confirm that the proposed method achieves a high accuracy of 98.8 on the CASIA Dataset B and outperforms the recent state-of-the-art methods.
Gait-based recognition techniques can be divided into two categories, marker-based and markerless methods. Using markers, in an early work, Kozlowski and Cutting @cite_0 @cite_42 attempted to attach a point-light display (marker-based method) to a human body to extract the gait information. With this system, a human observer can determine a subject’s gender based on the signals obtained with an acceptable level of accuracy (63 Today, owing to technical innovations in camera and sensor development, the human gait can be easily obtained without a point-light display, leading to the development of markerless methods. The markerless-based methods for gait recognition can be classified into the model and appearance-based approaches. Such categorization can also be used for gender classification.
@cite_0 @cite_42
56484167 2943148947 28660007 1984031350
Model-based Gait Recognition concerns identification using an underlying mathematical construct(s) representing the discriminatory gait characteristics (be they static or dynamic), with a set of parameters and a set of logical and quantitative relationships between them. These models are often simplified based on justifiable assumptions such as the system only accounts for pathologically normal gait. Such a system normally consists of gait capture, a model(s), a feature extraction scheme, a gait signature and a classifier (Figure 1). The model can be a 2- or 3-dimensional structural (or shape) model and motion model that lays the foundation for the extraction and tracking of a moving person. An alternative to a model-based approach is to analyse the motion of the human silhouette deriving recognition from the body’s shape and motion. A gait signature that is unique to each person in the database is then derived from the extracted gait characteristics. In the classification stage, many pattern classification techniques can be used, such as the k-nearest neighbour approach. Gait recognition is a leading remote-based identification method, suitable for real-world surveillance and medical applications. Model-based gait recognition methods have been particularly recognized due to their scale and view-invariant properties. We present the first model-based gait recognition methodology, @math lidar3DJ using a skeleton model extracted from sequences generated by a single flash lidar camera. Existing successful model-based approaches take advantage of high quality skeleton data collected by Kinect and Mocap, for example, are not practicable for applications outside the laboratory. The low resolution and noisy imaging process of lidar negatively affects the performance of state-of-the-art skeleton-based systems, generating a significant number of outlier skeletons. We propose a rule-based filtering mechanism that adopts robust statistics to correct for skeleton joint measurements. Quantitative measurements validate the efficacy of the proposed method in improving gait recognition. Gait analysis is a domain of interest in clinical medical practice, both for neurological and non-neurological abnormal troubles. Marker-based systems are the most favored methods of human motion assessment and gait analysis, however, these systems require specific equipment and expertise and are cumbersome, costly and difficult to use. In this paper we compare two low-cost and marker-less systems that are: (1) A Kinect in front of a treadmill and (2) a set of two camcorders on the sides of the treadmill, used to reconstruct the skeleton of a subject during walk. We validated our method with ground truth data obtained with markers manually placed on the subject’s body. Finally, we present an application for asymmetric gait recognition. Our results on different subjects showed that, compared to the Kinect, the two-camcorder approach was very efficient and provided accurate measurements for gait assessment. HighlightsPresentation of the new freely available TUM Gait from Audio, Image and Depth (GAID) database.Advancing gait based person identification by multimodal feature extraction.Gait based recognition of person traits: gender, age, height, shoe type.Baseline results and fusion for gait recognition using RGB, depth and audio. Recognizing people by the way they walk-also known as gait recognition-has been studied extensively in the recent past. Recent gait recognition methods solely focus on data extracted from an RGB video stream. With this work, we provide a means for multimodal gait recognition, by introducing the freely available TUM Gait from Audio, Image and Depth (GAID) database. This database simultaneously contains RGB video, depth and audio. With 305 people in three variations, it is one of the largest to-date. To further investigate challenges of time variation, a subset of 32 people is recorded a second time. We define standardized experimental setups for both person identification and for the assessment of the soft biometrics age, gender, height, and shoe type. For all defined experiments, we present several baseline results on all available modalities. These effectively demonstrate multimodal fusion being beneficial to gait recognition.