Showing papers in "IEICE ESS Fundamentals Review in 2021"
1 citations
TL;DR: Basic approaches to gait recognition are introduced, as well as recently deep-learning frameworks for it, and the practice of gait Recognition in forensics is addressed.
Abstract: Gait has been considered as a biometric cue for identifying a person, and hence, gait recognition has recently become an active research area. Unlike other biometrics, gait recognition is still applicable even if the target person is captured at a distance from the camera without being aware of the camera, and therefore, it is expected to applied to be forensics using CCTV footage. In this article, we introduce basic approaches to gait recognition, as well as recently deep-learning frameworks for it. Moreover, we address our practice of gait recognition in forensics. Finally, we conclude this article with perspectives on gait recognition in the future.
1 citations
TL;DR: This paper proposed a deterministic PSO without probabilistic elements and analyzed the dynamics of PSO using the dynamical systems theory and proposed a nonlinear map optimization (NMO) with improved local search capability.
Abstract: Particle swarm optimization (PSO) is one of the most effective optimization methods for the black-box optimization problem. PSO involves a large number of particles that share information with each other to search for an optimal solution. The method in which a large number of search individuals cooperate to search for an optimal solution is called swarm intelligence optimization. In group intelligence optimization, the balance between exploration and exploitation is important. However, in PSO, it is unclear to what extent each parameter affects exploration and exploitation. Therefore, we proposed a deterministic PSO without probabilistic elements and analyzed the dynamics of PSO using the dynamical systems theory. Each particle in deterministic PSO has its motion determined by its eigenvalue. To make this motion clearer, a canonical deterministic PSO on a regularized phase space was proposed. The results of these analyses clarified what is attributed to the parameters for exploration and exploitation, i.e., global and local search capabilities. On the basis of this fact, we proposed a nonlinear map optimization (NMO) with improved local search capability. In this paper, we present the background of our proposal and consider the solution-search capability of NMO.