Facial Recognition with the Super Pixel Entropy Estimation with the Virtual Assistance System

Authors

  • Tanuja Dhope Electronics and Communication, Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India. https://orcid.org/0000-0003-2907-8509
  • Rajneesh Tyagi Dean, Department of Agriculture, Sanskriti University, Mathura, Uttar Pradesh, India https://orcid.org/0000-0002-8571-6221
  • Vidya Nitin Patil Professor, Department of Civil Engineering, AISSMS COE PUNE, India
  • Veeramani Vijaya Raghavan ECE, Vignan's Foundation for Science, Technology and Research, Guntur, India
  • Tarun K. Sharma Professor & Dean, Department of Computer Science, Shobhit Institute of Engineering & Technology (Deemed to-be University), Meerut, India. https://orcid.org/0000-0002-9043-8641
  • Usha C. Pawar Assistant Professor, Mechanical Engineering, Datta Meghe College Of Engineering, Maharashtra, Navi Mumbai, India. https://orcid.org/0000-0002-6516-5354

Keywords:

Facial Recognition, Super pixel, Virtual Assistance, Machine Learning, Entrophy

Abstract

Face recognition system is the developing technology for the recent years. However, the traditional facial recognition system subjected to vast range of challenges due to variation in the structural features, color, and variation in the face. Traditionally, the facial recognition system comprises of the facial features with the landmark estimation. Bur those technique are not sufficient enough to extract the appropriate pixel in the identification of the features. This paper developed a Facial Super Pixel Entropy (FSPE) integrated with the virtual assistance for the identification of faces. The proposed FSPE model uses the segmentation of the super pixel in the facial images with the consideration of the entropy estimation. The performance of the proposed FSPE model is evaluated for the consideration of the different feature model for the processing, The analysis expressed that the proposed FSPE model achieves the segmentation accuracy of 99% and the accuracy is achieved as the 99%. This implies that propsoed FSPE model is effective for the facial recognition system with the virtual assistant.

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Tensor Flow in FSPET

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Published

19.12.2022

How to Cite

Dhope, T. ., Tyagi, R. ., Nitin Patil, V. ., Vijaya Raghavan, V. ., K. Sharma, T. ., & C. Pawar, U. . (2022). Facial Recognition with the Super Pixel Entropy Estimation with the Virtual Assistance System. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 226 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/2389

Issue

Vol. 10 No. 2s (2022)

Section

Research Article

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