An Separation of Different Facial Expression Recognition System
Nithin S1, Aravindan M B2, Abhilash Hegde3, Gururaj H L4
1Nithin S, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
2Aravindan M B, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
3Abhilash Hegde, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
4Gururaj H L, Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 438-441 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10900486S419/19©BEIESP | DOI: 10.35940/ijitee.F1090.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Face recognition is one of the hot topics in the current world and one of the popular topics of computer studies. Today face recognition in the network society and access to digital data is gaining more attention. The facial recognition system technology is a biometric assessment of a human’s face. There are many facial recognition techniques that are intended depending on facial expressions extraction, one of which is 3D facial recognition, as well as their fusion,is difficult. During preprocessing measures for picture recognition to remove only expression-specific characteristics from the face and prevent their issues with a convolution neural network. We can also use some theorems such as LBP and Taylor’s theorem to model face recognition. In particular, for cloud robots, we can also use this facial recognition on robots. The robot can perform functions and share data between servers and devices. Seven fundamental expressions are used to identify and classify: happiness, shock, fear, disgust, sadness, rage, and a neutral condition. Until now, the recognition rate is quite up to the expectation stage, but it still tries to enhance. To enhance the recognition frequency of facial image recognition, feelings are chosen by the vibrant Bayesian network technique to depict the development of facial awareness in addition to various emotional operations of facial expressions. The ICCA techniques involve various multivariate sets of distinct facial features that could be eyes, nose, and mouth.
Keywords: Facial Expression, Face Recognition, Expression, Emotions.
Scope of the Article: Computer Science and Its Applications