Procedure for Conventional Facial Emotion Detection Algorithms Based on Machine Learning

Year : 2023 | Volume : 01 | Issue : 01 | Page : 07-13
By

    Sachin T

  1. Komala K

  2. M.Z. Kurian

  1. Student, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India
  2. Assistant Professor, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India
  3. Head of Department, Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India

Abstract

Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. This study has recommended an Automatic Facial Expression Recognition System (AFERS). The proposed methodology consists of face detection, feature extraction, and facial expression identification processes. The initial phases of the face detection procedure include skin color identification using the YCbCr color model, illumination adjustment for face uniformity, and morphological operations for maintaining the required face region. Using the AAM (Active Appearance Model) approach, the first phase’s output is utilized to extract facial features such as the mouth, nose, and eyes. Automatic facial expression recognition is the third stage, and it is straightforward. Method of Euclidean Distance: This method compares the Euclidean distance between the feature points on the query image and the training images. The output picture expression is chosen based on the minimal Euclidean distance. This approach has a true recognition rate of between 90 and 95%. Utilizing the Artificial Neuro-Fuzzy Inference System (ANFIS), this method is further modified. In comparison to previous systems, this non-linear recognition system provides a recognition rate of close to 100%, which is satisfactory.

Keywords: Facial expression recognition (FER), multimodal sensor data, emotional expression recognition, spontaneous expression, real-world conditions

[This article belongs to International Journal of Electronics Automation(ijea)]

How to cite this article: Sachin T, Komala K, M.Z. Kurian Procedure for Conventional Facial Emotion Detection Algorithms Based on Machine Learning ijea 2023; 01:07-13
How to cite this URL: Sachin T, Komala K, M.Z. Kurian Procedure for Conventional Facial Emotion Detection Algorithms Based on Machine Learning ijea 2023 {cited 2023 Dec 16};01:07-13. Available from: https://journals.stmjournals.com/ijea/article=2023/view=129833

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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received May 16, 2023
Accepted July 18, 2023
Published December 16, 2023