In most pattern recognition applications, the object of interest is represented by a very high dimensional data-vector. High dimensionality of modeling vectors poses serious challenges related to the efficiency of retrieval, analysis and classifying the pattern of interest. The Curse of Dimension is a general reference to these challenges and commonly addressed by Dimension Reduction (DR) techniques. The most commonly used DR schemes are data-dependent like Principal Component Analysis (PCA). However, we may expect over-fitting and biasness of the adaptive models to the training sets as consequences of low sample density ratio to dimension. Therefore, data-independent DR schemes such as Random Projections (RP) are more desirable. In this paper, we investigate and test the performance of differently constructed overcomplete Hadamard-based mxn (m<<n) sub-matrices using Walsh-Paley (WP) matrices as a DR scheme for Gait-based Gender Classification (GBGC). In particular, we shall demonstrate that these Hadamard-based RPs perform as well as, if not better, PCA and Gaussian-based RPs. Moreover, we shall show that Walsh-Paley Structured Matrices (WPSM) perform better than Walsh-Paley Random Matrices (WPRM).
This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.
This paper presents gait recognition based on human skeleton and trajectory of joint points captured by Microsoft Kinect sensor. In this paper Two sets of dynamic features are extracted during one gait cycle: the first is Horizontal Distance Features (HDF) that is based on the distances between (Ankles, knees, hands, shoulders), the second set is the Vertical Distance Features (VDF) that provide significant information of human gait extracted from the height to the ground of (hand, shoulder, and ankles) during one gait cycle. Extracting these two sets of feature are difficult and not accurate based on using traditional camera, therefore the Kinect sensor is used in this paper to determine the precise measurements. The two sets of feature are separately tested and then fused to create one feature vector. A database has been created in house to perform our experiments. This database consists of sixteen males and four females. For each individual, 10 videos have been recorded, each record includes in average two gait cycles. The Kinect sensor is used here to extract all the skeleton points, and these points are used to build up the feature vectors mentioned above. K-nearest neighbor is used as the classification method based on Cityblock distance function. Based on the experimental result the proposed method provides 56% as a recognition rate using HDF, while VDF provided 83.5% recognition accuracy. When fusing both of the HDF and VDF as one feature vector, the recognition rate increased to 92%, the experimental result shows that our method provides significant result compared to the existence methods.
This paper presents a new algorithm for human gait recognition based on Spatio-temporal body biometric features
using wavelet transforms. The proposed algorithm extracts the Gait cycle depending on the width of boundary box
from a sequence of Silhouette images. Gait recognition is based on feature level fusion of three feature vectors: the gait
spatio-temporal feature represented by the distances between (feet, knees, hands, shoulders, and height); binary
difference between consecutive frames of the silhouette for each leg detected separately based on hamming distance; a
vector of statistical parameters captured from the wavelet low frequency domain. The fused feature vector is subjected
to dimension reduction using linear discriminate analysis. The Nearest Neighbour with a certain threshold used for
classification. The threshold is obtained by experiment from a set of data captured from the CASIA database. We shall
demonstrate that our method provides a non-traditional identification based on certain threshold to classify the outsider
members as non-classified members.