Abstract
Gait energy image (GEI) preserves the dynamic and static information of a gait sequence. The common static information includes the appearance and shape of the human body and the dynamic information includes the variation of frequency and phase. However, there is no consideration of the time that normalizes each silhouette within the GEI. As regards this problem, this paper proposed the accumulated frame difference energy image (AFDEI), which can reflect the time characteristics. The fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature. Then, gait recognition was accomplished using the nearest neighbor classifier based on the Euclidean distance. Finally, to verify the performance, the proposed algorithm was compared with the GEI + 2D-PCA and SFDEI + HMM on the CASIA-B gait database. The experimental results have shown that the proposed algorithm performs better than GEI + 2D-PCA and SFDEI + HMM and meets the real-time requirements.
Highlights
As one of the biometrics, the gait recognition is typically used for identifying an individual using image sequences, which capture a person’s walk
In regard that there is no consideration of the time that normalizes each silhouette in the Gait energy image (GEI), this paper proposes a new class energy image which is denoted as the accumulated frame difference energy image (AFDEI), and the fusion of the moment invariants extracted from GEI and AFDEI was selected as the gait feature
The moment invariants were extracted from GEI and AFDEI, and the characteristics were fused as the gait feature
Summary
As one of the biometrics, the gait recognition is typically used for identifying an individual using image sequences, which capture a person’s walk. Zhang et al [3] proposed a model-based approach to gait recognition by employing a five-link biped locomotion human model They extract the gait features from image sequences using the MetropolisHasting method, and Hidden Markov Models are trained based on the frequencies of these feature trajectories, from which recognition is performed. Silhouettebased approaches mainly extract the static or dynamic characteristics of the gait silhouette to recognize individuals through the moving information of image sequences without establishing prior model. Jeong and Cho [5] proposed a gait recognition method based on the multilinear tensor analysis They formed the accumulated silhouette from the gait image sequences and described those as the tensor. We recognized the individual using the nearest neighbor classifier based on the Euclidean distance
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