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In this paper, the impact of the lips for identity recognition is investigated. In fact, it is a challenging issue for identity recognition solely by the lips. In the first stage of the proposed system, a fast box filtering is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected through the proposed system, in which it is also able to resist shadow, beard, and rotation problems. For the feature extraction, two geometric ratios and ten parabolic-related parameters are adopted for further recognition through the support vector machine. Experimental results demonstrate that, when the number of subjects is fewer or equal to 29, the correct accept rate (CAR) is greater than 98%, and the false accept rate (FAR) is smaller than 0.066%. (CAR95.02%, FAR0.095% # Subjects ≤ 57). Moreover, the processing speed of the overall system achieves 34.43 frames per second, which meets the real-time requirement. Thus, the proposed system can be an effective candidate for facial biometrics applications when other facial organs are covered or when it is applied for an access control system.

In this paper, a temporal-reinforced approach to enhancing emotion recognition from facial images is presented. Shape and texture models of facial images are computed by using active appearance model (AAM), from which facial feature points and geometrical feature values are extracted. The extracted features are used by relevance vector machine (RVM) to recognize emotional states. We propose a temporal analysis approach to recognizing likelihood of emotional categories, such that more subtle emotion, such as degree and ratio of basic emotional states can be obtained. Furthermore, a method is developed to map the recognition result to the arousal-valence plane (A-V Plane). Experimental results verify that the performance of emotion recognition is enhanced by the proposed method.

This paper presents a mobile manipulation and visual servoing design for a configurable mobile manipulator by using a Kinect sensor. To achieve this goal, an image-based grasping design is combined with a visual simultaneous localization and mapping (vSLAM). The proposed vSLAM method is based on extended Kalman filter (EKF) and a Kinect RGBD sensor. In the manipulator design, the robot arms are designed to be able to configure as a handrail for user support in execution of walking assistance tasks. The purpose of this design is to perform mobile manipulation and walking assistance tasks on the same robot. Experiments on the prototype dual-arm mobile manipulator validate that the proposed method can find and grasp a target object after navigation a distance to the object position. After the robot delivered the object to the user, it configured to provide walking assist to the user using the suggested compliance controller.

In this paper, the impact of the lips for facial recognition is investigated. In the first stage of the proposed system, a Fast Box Filtering (FBF) is proposed to generate a noise-free source with high processing efficiency. Afterward, five various mouth corners are detected though the proposed system, in which it is also able to resist beard and rotation problems. For the feature extraction, two geometric ratios and 10 parabolic related parameters are adopted for further recognition through the Support Vector Machine (SVM). Experimental results demonstrate that when the number of subjects is fewer or equal to 36, the Correct Accept Rate (CAR) is greater than 98%, and the False Accept Rate (FAR) is smaller than 0.064% (CAR>95.6%, FAR