An comparative Study on Facial Character Analysis

Kavya P; Kamalraj R

doi:10.65301/iitm.2021.12.1.485

Articles Volume 12 Issue 1 January-June 2021

DOI 10.65301/iitm.2021.12.1.485

An comparative Study on Facial Character Analysis

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60 Downloads

Published 2021-01-30

Pages 66-68

Abstract

This work tends to present a new plan to investigate face character expression by exploring some common and specific data among totally different expressions impressed by the observation that only many facial elements are active in expression revelation. An automatic Facial Character Recognition features has been performed in the domain of Computer Human Interaction. Detection of facial character has be implemented with CNN. This can be accomplished with testing the real time images or with the given dataset that detects a range of Five facial expressions with training and validating in the given images.

Keywords

Character Recognition Computer Human Interaction classification Image Quality dataset Recognition Accuracy.

References

A. T. a. D. A. E. a. O.-S. T. Lopes, A facial expression recognition system using convolutional networks, 2015.
. a. J. W. K. Chen, Facial recognition with PCA and machine learning methods, 2017.
G. a. B. M. a. F. I. a. C. J. a. M. J. Little wort, Analysis of machine learning methods for real-time recognition of facial expressions from video, 2004.
M. N. a. C. G. W. a. P. C. a. A. R. Dailey, EMPATH: A neural network that categorizes facial expressions, 2002.
M. a. P.-R. V. a. M. R. a. B. M. Abouelenien, Multimodal gender detection, 2017.
S. a. S. S. a. K. J. Kumar, A study on face recognition techniques with age and gender classification, 2017.
D. a. T. M. Fidaleo, “Manifold analysis of facial gestures for face recognition,” Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, 2003.
S. V. a. R. A. T. a. T. V. A. a. M. T. P. a. K. K. C. a. K. S. D. Ioannou, “Emotion recognition through facial expression analysis based on a neuro fuzzy network,” Neural Networks, 2005.
M. F. a. J. B. a. M. M. a. P. M. a. S. K. Valstar, “The first facial expression recognition and analysis challenge,” Face and Gesture 2011, 2011.

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Kavya P, Kamalraj R . (2021). An comparative Study on Facial Character Analysis. IITM Journal of Management and IT, 12(1`), 66-68.

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IITM Journal of Management and IT

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[1] A. T. a. D. A. E. a. O.-S. T. Lopes, A facial expression recognition system using convolutional networks, 2015.

[2] . a. J. W. K. Chen, Facial recognition with PCA and machine learning methods, 2017.

[3] G. a. B. M. a. F. I. a. C. J. a. M. J. Little wort, Analysis of machine learning methods for real-time recognition of facial expressions from video, 2004.

[4] M. N. a. C. G. W. a. P. C. a. A. R. Dailey, EMPATH: A neural network that categorizes facial expressions, 2002.

[5] M. a. P.-R. V. a. M. R. a. B. M. Abouelenien, Multimodal gender detection, 2017.

[6] S. a. S. S. a. K. J. Kumar, A study on face recognition techniques with age and gender classification, 2017.

[7] D. a. T. M. Fidaleo, “Manifold analysis of facial gestures for face recognition,” Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications, 2003.

[8] S. V. a. R. A. T. a. T. V. A. a. M. T. P. a. K. K. C. a. K. S. D. Ioannou, “Emotion recognition through facial expression analysis based on a neuro fuzzy network,” Neural Networks, 2005.

[9] M. F. a. J. B. a. M. M. a. P. M. a. S. K. Valstar, “The first facial expression recognition and analysis challenge,” Face and Gesture 2011, 2011.