Deep Neural Network Framework for Hospital Queue Management

Authors

  • K. Kamatchi Research Scholar, Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, India
  • E. Uma Assistant Professor, Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, India

Keywords:

Convolution Neural Network, Deep Learning approach, Deep Convolution Neural Network, Face recognition, Face verification, Face identification, Gender recognition, Pretrained model, Transfer Learning, Visual Geometry Group Network

Abstract

Recognition is the process of identification or checking the identity of a person who uses his face. Automatic face and gender recognition from facial images are used to expand its use in various programs and teams, notably through the development of online sites for human interaction and web-based media. In our proposed model the whole process is to develop a facial recognition. This model consists of several key steps which are developed using the most advanced techniques: Multi-task Cascaded Convolutional Networks for facial detection and Convolution Neural Network (CNN) for generating face embeddings. This proposed system is the practical application of these next generation deep learning approaches for facial recognition tasks. However, the performance of the existing system with the images of the physical world face, images, is not entirely excellent, especially when compared to the result of the face detection task. With the Deep Convolution of Neural Networks (DCNN) process, a development in grouping tasks according to gender classification that is a motivation behind the choice, a Proposed Convolution Neural Network Visual Geometry Group Network (VGGNet) architecture that can be used in extreme cases when the amount of training data used to learn DCNNs based on the VGGNet architecture is limited.

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Published

27-11-2022

Issue

Section

Articles

How to Cite

[1]
K. Kamatchi and E. Uma, “Deep Neural Network Framework for Hospital Queue Management”, IJRESM, vol. 5, no. 11, pp. 136–142, Nov. 2022, Accessed: Jun. 17, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2440