Multimodal Biometrics is the usage of multiple biometric indicators by personal identification systems for identifying individuals. It is applied to secure and authenticate the biometric data, enhance accuracy of recognition and reduce bandwidth. This paper presents a robust multimodal biometric image watermarking scheme using Particle Swarm Optimization (PSO). The key idea is to watermark an i...

Activities and events in our lives are structural, be it a vacation, a camping trip, or a wedding. While individual details vary, there are characteristic patterns that are specific to each of these scenarios. For example, a wedding typically consists of a sequence of events such as walking down the aisle, exchanging vows, and dancing. In this paper, we present a data-driven approach to learnin...

Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles in PSO is extended by using the near-neighbor effect in mechanical theory, which is a universal phenomenon in nature and society. In the proposed near-neighbor effect based forc...

Speaker recognition in real environment with reliable mode is a key challenge for ubiquitous service in human computer interface. In this paper, we present a robust multimodal speaker identification system with optimized reliability of different modalities. We propose an extension of modified convection function’s optimizing factors to account optimum reliability simultaneously in audio, face a...

This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal funct...

This paper demonstrates a new approach to multidimensional path planning that is based on multiresolution path representation, where explicit configuration space computation is not required, and incorporates an evolutionary algorithm for solving the multimodal optimization problem, generating multiple alternative paths simultaneously. The multiresolution path representation reduces the expected...

This paper presents an unsupervised approach to feature binary coding for efficient semantic image retrieval. Although the majority of the existing methods aim to preserve neighborhood structures of the feature space, semantically similar images are not always in such neighbors but are rather distributed in non-linear low-dimensional manifolds. Moreover, images are rarely alone on the Internet ...