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Yang Li
Key Lab. of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang, China

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Journal article
Published: 20 September 2020 in Neural Networks
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In this paper, we propose a new face de-identification method based on generative adversarial network (GAN) to protect visual facial privacy, which is an end-to-end method (herein, FPGAN). First, we propose FPGAN and mathematically prove its convergence. Then, a generator with an improved U-Net is used to enhance the quality of the generated image, and two discriminators with a seven-layer network architecture are designed to strengthen the feature extraction ability of FPGAN. Subsequently, we propose the pixel loss, content loss, adversarial loss functions and optimization strategy to guarantee the performance of FPGAN. In our experiments, we applied FPGAN to face de-identification in social robots and analyzed the related conditions that could affect the model. Moreover, we proposed a new face de-identification evaluation protocol to check the performance of the model. This protocol can be used for the evaluation of face de-identification and privacy protection. Finally, we tested our model and four other methods on the CelebA, MORPH, RaFD, and FBDe datasets. The results of the experiments show that FPGAN outperforms the baseline methods.

ACS Style

Jiacheng Lin; Yang Li; Guanci Yang. FPGAN: Face de-identification method with generative adversarial networks for social robots. Neural Networks 2020, 133, 132 -147.

AMA Style

Jiacheng Lin, Yang Li, Guanci Yang. FPGAN: Face de-identification method with generative adversarial networks for social robots. Neural Networks. 2020; 133 ():132-147.

Chicago/Turabian Style

Jiacheng Lin; Yang Li; Guanci Yang. 2020. "FPGAN: Face de-identification method with generative adversarial networks for social robots." Neural Networks 133, no. : 132-147.

Journal article
Published: 01 January 2020 in IEEE Access
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As the problem of an aging population becomes more and more serious, social robots have an increasingly significant influence on human life. By employing regular question-and-answer conversations or wearable devices, some social robotics products can establish personal health archives. But those robots are unable to collect diet information automatically through robot vision or audition. A healthy diet can reduce a person’s risk of developing cancer, diabetes, heart disease, and other age-related diseases. In order to automatically perceive the dietary composition of the elderly by listening to people’s chatting, this paper proposed a chat-based automatic dietary composition perception algorithm (DCPA). DCPA uses social robot audition to understand the semantic information and percept dietary composition for Mandarin Chinese. Firstly, based on the Mel-frequency cepstrum coefficient and convolutional neural network, a speaker recognition method is designed to identify speech data. Based on speech segmentation and speaker recognition algorithm, an audio segment classification method is proposed to distinguish different speakers, store their identity information and the sequence of expression in a speech conversation. Secondly, a dietetic lexicon is established, and two kinds of dietary composition semantic understanding algorithms are proposed to understand the eating semantics and sensor dietary composition information. To evaluate the performance of the proposed DCPA algorithm, we implemented the proposed DCPA in our social robot platform. Then we established two categories of test datasets relating to a one-person and a multi-person chat. The test results show that DCPA is capable of understanding users’ dietary compositions, with an F1 score of 0.9505, 0.8940 and 0.8768 for one-person talking, a two-person chat and a three-person chat, respectively. DCPA has good robustness for obtaining dietary information.

ACS Style

Zhidong Su; Yang Li; Guanci Yang. Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese. IEEE Access 2020, 8, 8768 -8782.

AMA Style

Zhidong Su, Yang Li, Guanci Yang. Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese. IEEE Access. 2020; 8 (99):8768-8782.

Chicago/Turabian Style

Zhidong Su; Yang Li; Guanci Yang. 2020. "Dietary Composition Perception Algorithm Using Social Robot Audition for Mandarin Chinese." IEEE Access 8, no. 99: 8768-8782.

Journal article
Published: 14 January 2019 in Remote Sensing
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In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.

ACS Style

Guanci Yang; Zhanjie Chen; Yang Li; Zhidong Su. Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sensing 2019, 11, 149 .

AMA Style

Guanci Yang, Zhanjie Chen, Yang Li, Zhidong Su. Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm. Remote Sensing. 2019; 11 (2):149.

Chicago/Turabian Style

Guanci Yang; Zhanjie Chen; Yang Li; Zhidong Su. 2019. "Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm." Remote Sensing 11, no. 2: 149.

Conference paper
Published: 01 July 2018 in 2018 13th World Congress on Intelligent Control and Automation (WCICA)
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Different smart products produced by various manufacturers work on dissimilar communication standards incompatible with one other. Such incompatibility renders communication of products challenging and serve as a severe hindrance to the development of the smart home industry. Considering the issue and advantage of the social robot, this research investigates a Home Assistant-based collaborative framework of multi-sensor fusion for the smart home social robot. We review the related work and briefly introduce the Home Assistant platform. Then, we detail the system design and its implementation. To check system performance, we designed two test cases with different scenarios. Finally, we detail the test results, which indicates that the system can fuse the multi-sensors and performs satisfactorily according to the designed control logic.

ACS Style

Yang Li; Weihua Sheng; Guanci Yang; Baojuan Liang; Zhidong Su; Zhanjie Chen. Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*. 2018 13th World Congress on Intelligent Control and Automation (WCICA) 2018, 401 -406.

AMA Style

Yang Li, Weihua Sheng, Guanci Yang, Baojuan Liang, Zhidong Su, Zhanjie Chen. Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*. 2018 13th World Congress on Intelligent Control and Automation (WCICA). 2018; ():401-406.

Chicago/Turabian Style

Yang Li; Weihua Sheng; Guanci Yang; Baojuan Liang; Zhidong Su; Zhanjie Chen. 2018. "Home Assistant-Based Collaborative Framework of Multi-Sensor Fusion for Social Robot*." 2018 13th World Congress on Intelligent Control and Automation (WCICA) , no. : 401-406.