Comparative Analysis of Facial Expression Detection Techniques Based on Neural Network
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https://doi.org/10.14419/ijet.v7i4.38.27597
Received date: February 20, 2019
Accepted date: February 20, 2019
Published date: December 3, 2018
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Object Detection, Robotics, Pattern Recognition, Neural Network, Facial Expression, Computer Vision -
Abstract
Face detection is a critical part of vision and a robot needs to identify a human accurately. A human face undergoes several states of facial expression in a day. Many object detection techniques are applied to identify a facial expression from a digital image or a video frame. Each object detection technique has its own benefits. The overall objective of this paper is to explore the benefits and limitation of existing techniques and provide a comparative analysis. Neural network based facial expression detection technique has demonstrated potential benefits over existing facial expression detection techniques.
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References
- Michel Owayjan, Roger Achkar and Moussa Iskanda ,“Face Detec-tion with Expression Recognition using Artificial Neural Net-works”, IEEE, 2016
- Chenghao Zheng ,Menglong Yang and Chengpeng Wang, “A Re-al-Time Face Detector Based on an End-to-End CNN” , IEEE, 2017
- Jiajun Wang, Beizhan Wang, Yinhuan Zheng and Weiqiang Liu, “Research and Implementation on Face Detection Approach Based on Cascaded Convolutional Neural Networks’, IEEE, 2017
- Kaihao Zhang,Yongzhen Huang, Hong Wu and Liang Wang, “Facial smile detection based on deep learning features” , IEEE, 2015
- Xin Guo, Luisa Polania and Kenneth Barner , “Smile Detection in the Wild Based on Transfer Learning”, IEEE, 2018
- Chi Cuong Nguyen, Giang Son Tran, Thi Phuong Nghiem, Nhat Quang Doan, Damien Gratadour, Jean Christophe Burie, Chi Mai Luong, “Towards Real-Time Smile Detection Based on Faster Region Convolutional Neural Network” , IEEE, 2018
- Nikolay Neshov and Agata Manolova , “Pain detection from facial characteristics using supervised descent method”, IEEE, 2015
- Weihong Deng, Jiani Hu, Shuo Zhang and Jun Guo ,“DeepEmo: Real-world facial expression analysis via deep learning”, IEEE, 2015
- Talia Tron, Abraham Peled, Alexander Grinsphoon and Daphna Weinshall , “Facial expressions and flat affect in schizophrenia, automatic analysis from depth camera data”, IEEE, 2016
- Carla M. C. Paxiuba and Celson P. Lima, “A methodological ap-proach — Working emotions and learning using facial expres-sions”, IEEE, 2018
- Gloria Zen, Lorenzo Porzi, Enver Sangineto, Elisa Ricci and Nicu Sebe, “Learning Personalized Models for Facial Expression Anal-ysis and Gesture Recognition”, IEEE, 2016
- Aditya Kamath, Aradhya Biswas and Vineeth Balasubramanian, “A crowdsourced approach to student engagement recognition in e-learning environments”, IEEE, 2016
- Petr aloun, Jakob Stonawski and Ivan Zelinka, “Recommending New Links in Social Networks Using Face Recognition”, IEEE, 2013
- Samira Reihanian, Ehsan Arbabi and Behrouz Maham, “Random sparse representation for thermal to visible face recognition”, IEEE, 2017
- Siti Nurhana Abd Wahab, Suzaimah Ramli and Norulzahrah Mohd Zainudin, “Temperature determining method from motion detection using thermal images”, IEEE, 2015
- Chule Yang, Danwei Wang and Prarinya Siritanawan, “Organ-Based Facial Verification Using Thermal Camera”, IEEE, 2016.
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How to Cite
Mohan, Y., & Tripathi, V. (2018). Comparative Analysis of Facial Expression Detection Techniques Based on Neural Network. International Journal of Engineering and Technology, 7(4.38), 866-870. https://doi.org/10.14419/ijet.v7i4.38.27597
