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.
In the model-based methods @cite_29 @cite_20 @cite_19 , the human body is divided into various parts, the structures of which are then fitted using primitive shapes such as ellipses, rectangles, and cylinders. Then, the gait feature is encoded using the parameters of the primitive shapes to measure the time-varying motion of the subject. In @cite_29 , L. divide a human silhouette into seven different parts corresponding to the head and shoulder region, the front of the torso, back of the torso, front thigh, back thigh, front calf and foot, and back calf and foot. They then use ellipses to fit the model and capture the parameters of the ellipses such as the mean, standard deviation, orientation, and magnitude of the major components as feature vectors for classification. Although such methods are robust to noise and occlusions, they typically require a relatively high computational cost.
@cite_19 @cite_29 @cite_20
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Gait based gender identification has received a great attention from researchers in the last decade due to its potential in different applications. This will help a human identification system to focus only on the identified gender related features, which can improve search speed and efficiency of the retrieval system by limiting the subsequent searching space into either a male database or female database. In this paper after preprocessing, four binary moment features and two spatial features are extracted from human silhouette. Then the extracted features are used for training and testing two different pattern classifiers kNearest Neighbor (kNN) and Support Vector Machine(SVM). Experimental results show superior performance of our approach to the existing gender classifiers. To evaluate the performance of the proposed algorithm experiments have been conducted on NLPR database. This paper describes a set of representations of gait appearance features for the purpose of person identification. Our gait representation has two stages: the first stage computes a set of image features that are based on moments extracted from orthogonal view video silhouettes of human walking motion; the second stage applies three methods of aggregating these image features over time to create the gait sequence features. Despite their simplicity, the resulting gait sequence feature vectors contain enough information to perform well on human identification. We demonstrate the accuracy of recognition using gait video sequences collected over different daysa nd times, under varying lighting environments an d explore the differences in the three time-aggregation methods for the purpose of recognition. 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.