Evaluation of 3d facial paralysis using fuzzy logic

  • Abstract
  • Keywords
  • References
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  • Abstract

    Face recognition are of great interest to researchers in terms of Image processing and Computer Graphics. In recent years, various factors become popular which clearly affect the face model. Which are ageing, universal facial expressions, and muscle movement. Similarly in terms of medical terminology the facial paralysis can be peripheral or central depending on the level of motor neuron lesion which can be below the nucleus of the nerve or supra nuclear. The various medical therapy used for facial paralysis are electroaccupunture, electrotherapy, laser acupuncture, manual acupuncture which is a traditional form of acupuncture. Imaging plays a great role in evaluation of degree of paralysis and also for faces recognition. There is a wide research in terms of facial expressions and facial recognition but limited research work is available in facial paralysis. House- Brackmann Grading system is one of the simplest and easiest method to evaluate the degree of facial paralysis. During evaluation common facial expressions are recorded and are further evaluated by considering the focal points of the left or the right side of the face. This paper presents the classification of face recognition and its respective fuzzy rules to remove uncertainty in the result after evaluation of facial paralysis.



  • Keywords

    Stages of Face Recognition; 3D Face Recognition; CNN; Evaluation of Facial Paralysis; MAMDANI Model.

  • References

      [1] Hyunjong Cho et.al. “An efficient hybrid face recognition algorithm using PCA and GABOR wavelets”, International Journal of advanced Robotics systems, 2014. https://doi.org/10.5772/58473.

      [2] Issam Dagher et.al. “Face recognition using the most representative SIFT Images”, International Journal of Signal processing, image processing and pattern recognition, Vol.7, Issue: 1, 2014.

      [3] Aruni RoyChowdhury “One to many face recognition with Bi-linear CNNs”,IEEE Conference on application of computer Vision, Published in March 2016.

      [4] Avenir K.Troitsky, “Two-level multiple face detection algorithm based on Local feature Search and structure recognition methods”, International journal of applied engineering research, Vol.11 No.6, 2016.

      [5] Byoung Chul Ko, “A brief review of facial emotion recognition based on visual information” Sensors”, January 2018. https://doi.org/10.3390/s18020401.

      [6] Narayan T. Deshpande “Face detection and recognition using Viola-Jones algorithm and fusion of PCA and ANN”, Advances in Computational sciences and technology”, Vol.10, No.5, 2017.

      [7] Manjunatha Hiremath, “Current trends on face recognition methods in face biometrics” International journal of computer engineering and applications, Vol.12, No.1 January 2018.

      [8] Xiangyi Kong,et.al., “Automatic detection of Acromegaly from facial photographs using machine learning methods” “EBioMedicine” 2018. https://doi.org/10.1016/j.ebiom.2017.12.015.

      [9] Hwai-Jung Hsu,et.al . “Face recognition on Drones: Issues and limitations”,Institute of Information Science,Academic Sinica.

      [10] Raunak Dave,et.al. “Face recognition techniques: A survey”, March 2018.

      [11] Sudha Narang, “Comparison of face recognition algorithms using Opencv for attendance system” International journal of scientific and research publication, Vol.8, No.2, Feb.2018.

      [12] STM Siregar, et.al “Human face recognition using Eigen face in cloud computing environment”, 10th International conference Numerical analysis in engineering, 2018.

      [13] Claire L .Witham, “Automated face recognition of rhesus macaques” Journal of neuroscience method, July 2017, Elsevier.

      [14] Suparna Biswas, et.al. “An efficient face recognition method using contourlet and curvelet transform”, Journal of king saud University-Computer and information science, 2017.

      [15] Pratibha Sukhija, et.al. “Face recognition system using genetic algorithm”, International conference on computational modelling and security (CMS2016), Science direct. https://doi.org/10.1016/j.procs.2016.05.183.




Article ID: 13619
DOI: 10.14419/ijet.v7i4.13619

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