Initial Test Results of the Omron Face Cue Entry System at the University of Missouri-Rolla Reactor

Tokuhiro, Akira; Vaughn, Brian J.

doi:10.3327/jnst.41.502

Cited by 2 publications

(2 citation statements)

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“…Therefore, data fusion is often used to dynamically localize the robot and to construct representations of the dynamically changing environment. 9 Analyzing the uncertainties in the perception models of the vision system and the accumulative total fault in dead reckoning systems for the mobile robot, 10 a Bayesian Belief Network is proposed here to fuse data from odometer and the PCS for the robot's selflocalization at successive observation points, as shown in Fig. 7.…”

Section: Multidata Fusion Algorithmmentioning

confidence: 99%

Multiagent cooperation based entertainment robot

et al. 2006

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In recent years, cooperation of heterogeneous multiagents (HMAs) has achieved formidable results and gained an increasing attention by researchers. This paper presents an entertainment robot system based on the HMAs' cooperation. This robot is used to distinguish the basic information of a person from the audience and entertain him in the exhibition hall. The main agents include the Arbitration System (AS), the Face Recognition System (FRS), the Position Cognition System (PCS), and the Behavior Planner (BP), which are carved up according to their respective functions. Each agent completes its own task independently and the robot finishes the whole mission through their cooperation. The hybrid control architecture and the data-fusion algorithm based on Bayesian Belief Network are also discussed in this paper.

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Section: Multidata Fusion Algorithmmentioning

confidence: 99%

Multiagent cooperation based entertainment robot

et al. 2006

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“…The face recognition has many applications in various fields including government and business. Generally these applications can be divided into the two groups: (1) Authentication: to verify users in accessing something, for example, in securing a nuclear reactor [1]. In this application, face recognition has many benefits compared to the traditional passwords, such as preventing forgotten passwords or misuse of stolen keys.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Performance in Facing Pose Variation

Gunawan

Prasetyo

2017

CommIT (Communication and Information Technology) Journal

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There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.

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Initial Test Results of the Omron Face Cue Entry System at the University of Missouri-Rolla Reactor

Cited by 2 publications

References 5 publications

Multiagent cooperation based entertainment robot

Multiagent cooperation based entertainment robot

Face Recognition Performance in Facing Pose Variation

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