Genetic feature selection for gait recognition

Abstract

Abstract. Many research studies have demonstrated that gait can serve as a useful biometric modality for human identification at a distance. Traditional gait recognition systems, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised performance. We investigate the problem of selecting a subset of the most relevant gait features for improving gait recognition performance. This is achieved by discarding redundant and irrelevant gait features while preserving the most informative ones. Motivated by our previous work on feature subset selection using genetic algorithms (GAs), we propose using GAs to select an optimal subset of gait features. First, features are extracted using kernel principal component analysis (KPCA) on spatiotemporal projections of gait silhouettes. Then, GA is applied to select a subset of eigenvectors in KPCA space that best represents a subject's identity. Each gait pattern is then represented by projecting it only on the eigenvectors selected by the GA. To evaluate the effectiveness of the selected features, we have experimented with two different classifiers: k nearest-neighbor and Naïve Bayes classifier. We report considerable gait recognition performance improvements on the Georgia Tech and CASIA databases.

Utilizing Spatio Temporal Gait Pattern and Quadratic SVM for Gait Recognition

“…The gait silhouette image is adapted to analyze gait motion flow as a sequence of images [70,71]. In [72], gait silhouette images of the complete gait cycle are utilized to create one "Bulk motion gait silhouette image". The contours of the gait silhouette are previously adapted for shape analysis based [73,74], deformable active contours based [75], normal distance maps based [66], and posture and contour analysisbased gait recognition [76].…”

Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimizing the Classification of Mobility Data in Health Informatics

“…Feature selection and dimensionality reduction methods have been studied in the gait domain for a variety of applications, including activity recognition [13] and classification of different populations' gait [2,35]. These techniques have been used for identifying the best subset of features from the gait data collected using infrared markers [1,2], video images [32], ground reaction force mats [35], and wearable sensors [5,23]. Among the studies that used the data collected from wearable sensors to distinguish between the gait of healthy individuals and pathological gait, Caramia et al [5] investigates the various subsets of features on several classifiers' performance and reports that the subset of features built using PCA outperforms the other subsets of features made using the domain knowledge.…”

Gait Recognition Based on Silhouettes Sequences and Neural Networks for Human Identification

“…George Bebis, et al [9] employed a temporal contour projection as gait pattern to extract required feature vectors for classification. Eigenvectors calculated by applying (KPCA) on four gait patterns of each subject yielded a large number of features that can be reduced with Genatics Algorithm (GA ).…”