Explore the vast range of titles available.

This study proposes a robust method for the face recognition from low‐resolution red, green, and blue‐depth (RGB‐D) cameras acquired images which have a wide range of variations in head pose, illumination, facial expression, and occlusion in some cases. The local binary pattern (LBP) of the RGB‐D images with the suitable feature dimension of Depth image is employed to extract the facial features. On the basis of error correcting output codes, they are fed to multiclass support vector machines (MSVMs) for the off‐line training and validation, and then the online classification. The proposed method is called as the LBP‐RGB‐D‐MSVM with the suitable feature dimension of the depth image. The effectiveness of the proposed method is evaluated by the four databases: Indraprastha Institute of Information Technology, Delhi (IIIT‐D) RGB‐D, visual analysis of people (VAP) RGB‐D‐T, EURECOM, and the authors. In addition, an extended database merged by the first three databases is employed to compare among the proposed method and some existing two‐dimensional (2D) and 3D face recognition algorithms. The proposed method possesses satisfactory performance (as high as 99.10 ± 0.52% for Rank 5 recognition rate in their database) with low computation (62 ms for feature extraction) which is desirable for real‐time applications.

At beginning, the kinematic model and dynamic model of a differential mobile robot (DMR), and the dynamic model of left- and right-wheel DC motors are combined to be the controlled system. The control inputs of the proposed controlled system are the input voltages for the left- and right-wheel motors. The (indirect) outputs are the two-dimensional (2D) position and orientation of a DMR. Owing to the under-actuated characteristic, the direct reference input (i.e. two desired motor currents) using the first sliding surface is designed, so that the 2D position and orientation of the DMR are simultaneously controlled by two motor currents (i.e. the direct output). On the other hand, the second sliding surface is designed as the linear dynamics of tracking error of motor currents. Under completely (or partially) known frictions and uncertainties, the hierarchical sliding-mode under-actuated control with suitable conditions is designed, such that two motor currents asymptotically track two desired motor currents, respectively. Then the asymptotic tracking for the 2D position and orientation of a DMR is achieved. The simulations for various trajectories, completely and partially known frictions and uncertainties, and control parameters are presented to evaluate the effectiveness and robustness of the proposed method.

This paper aims at attaining one-leader & two-followers (1L-2F) formation control of multi-nonholonomic mobile robot (multi-NMR) systems subject to uncertainties and, at the same time, achieves trajectory-tracking of the leader NMR. To begin, the tracking error between the leader and a virtual reference robot is defined. Then, the extension to a leader-follower formation control structure is utilized to define the formation error (i.e., separation and orientation errors) between the leader and the followers. It has been proven that fuzzy sliding-mode tracking control (FSMTC) and backstepping formation control (BFC) can improve performance and stability when the overall closed-loop system is subject to uncertainties. Therefore, FSMTC and BFC are used for trajectory tracking of the leader NMR and formation control for two followers with respect to the leader, respectively. The stability of the closed-loop multi-NMR systems, i.e., trajectory tracking and formation control, is demonstrated through Lyapunov stability criteria. Finally, to validate the theoretical developments, computer simulations are conducted which prove the effectiveness, efficiency and robustness of the proposed scheme.

It is realized that fixed thresholds mostly fail in two circumstances as they only search for a certain range of skin color: (i) any skin-like object may be classified as skin if skin-like colors belong to fixed threshold range; (ii) any true skin for different races may be mistakenly classified as non-skin if that skin colors do not belong to fixed threshold range. In this paper, graph cuts (GC) is first extended to skin color segmentation. Although its result is acceptable, a complex environment with skin-like objects or different skin colors or different lighting conditions often results in a partial success. It is also known that probability neural network (PNN) has the advantage of recognizing different skin colors in cluttered environments. Therefore, many images with skin-like objects or different skin colors or different lighting conditions are segmented by the proposed algorithm (i.e., the combination of GC algorithm and PNN classification with other functions, e.g., morphology filtering, labeling, area constraint). The compared results among GC algorithm, PNN classification, and the proposed algorithm are presented not only to verify the accurate segmentation of these images but also to reduce the computation time. Finally, the application to the classification of hand gestures in complex environment with different lighting conditions further confirms the effectiveness and efficiency of our method.

This paper aims at attaining one-leader & two-followers (1L-2F) formation control of multi-nonholonomic mobile robot (multi-NMR) systems subject to uncertainties and, at the same time, achieves trajectory-tracking of the leader NMR. To begin, the tracking error between the leader and a virtual reference robot is defined. Then, the extension to a leader-follower formation control structure is utilized to define the formation error (i.e., separation and orientation errors) between the leader and the followers. It has been proven that fuzzy sliding-mode tracking control (FSMTC) and backstepping formation control (BFC) can improve performance and stability when the overall closed-loop system is subject to uncertainties. Therefore, FSMTC and BFC are used for trajectory tracking of the leader NMR and formation control for two followers with respect to the leader, respectively. The stability of the closed-loop multi-NMR systems, i.e., trajectory tracking and formation control, is demonstrated through Lyapunov stability criteria. Finally, to validate the theoretical developments, computer simulations are conducted which prove the effectiveness, efficiency and robustness of the proposed scheme.