Face Detection and Recognition using MTCNN and FaceNet

Year : 2024 | Volume :11 | Issue : 02 | Page : –
By

Sahil Bisht,

Sonal Fatangare,

Apeksha Patil,

Aakanksha Panadi,

Dev Kulkarni,

  1. Student Department of Computer Engineering, Rasiklal M. Dhariwal Sinhgad Technical Institutes Campus, Pune Maharashtra India
  2. Assistant Professor Department of Computer Engineering, Rasiklal M. Dhariwal Sinhgad Technical Institutes Campus, Pune Maharashtra India
  3. Student Department of Computer Engineering, Rasiklal M. Dhariwal Sinhgad Technical Institutes Campus, Pune Maharashtra India
  4. Student Department of Computer Engineering, Rasiklal M. Dhariwal Sinhgad Technical Institutes Campus, Pune Maharashtra India
  5. Student Department of Computer Engineering, Rasiklal M. Dhariwal Sinhgad Technical Institutes Campus, Pune Maharashtra India

Abstract

Face detection and face recognition are major tasks in the field of computer vision with several real-world applications and many products being developed in the same field. This paper gives a detailed implementation of the product that is developed for accurate detection and recognition of faces along with audio output of the face detected. This development would act as a base for a few future products that can be developed for the visually impaired community. For the development of the product, a thorough survey was done from classical methods, such as eigenfaces and Fisher faces, to cutting-edge deep learning techniques, such as advanced convolutional neural networks analyzing various face detection and recognition algorithms and the challenges associated with them, like changing lighting scenarios, obstructed views, and pose fluctuations. Generally, Convolutional Neural Networks (CNNs) are employed in developing such products. However, instead of utilizing general CNN, this product utilizes specific algorithms, namely Multi-Task Cascaded Convolutional Neural network (MTCNN) for face detection and FaceNet for face recognition respectively which are based on CNN itself. The model is trained on images of different persons which helps in extracting the required facial features. Finally, when the program is run, the detected face is shown with a green bounding box and the name of the person detected at the right bottom of the bounding box. Also, an audio output is provided by the system giving the name of the detected person. If no person is detected, then “Unknown” is the audio output given. This product gives a high accuracy due to extra filter layers in both MTCNN and FaceNet which helps in refining the training process and improving the efficiency of the system.

Keywords: Face Recognition, Face Detection, Convolutional Neural Network (CNN), Deep Learning, MTCNN, FaceNet, Industrial Applications.

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni. Face Detection and Recognition using MTCNN and FaceNet. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):-.
How to cite this URL: Sahil Bisht, Sonal Fatangare, Apeksha Patil, Aakanksha Panadi, Dev Kulkarni. Face Detection and Recognition using MTCNN and FaceNet. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155859

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Regular Issue Subscription Review Article
Volume 11
Issue 02
Received May 28, 2024
Accepted July 5, 2024
Published July 10, 2024
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