Facial Expression Recognition Using Python Using CNN Model
Akash Kumar, Athira B. Nair, Swarnaprabha Jena, Debaraj Rana, Subrat Kumar Pradhan
Current Journal of Applied Science and Technology · pp. 7–16 · Published 24 Aug 2021
10.9734/cjast/2021/v40i2031459Abstract
Facial expressions are a vital part of human life. Each day has a number of instances and all instances include numerous amounts of communication. Every communication expressed with emotion tells us about the state of the person. The interpersonal as well as security purposes are solved through facial expressions. The mischievous intention of a person can be caught by his expressions. The human mind can capture visual information faster. So, a machine recognizing it will be a challenge. As the saying goes- “A picture is worth a thousand words”- only when it is represented well. A machine being able to detect the atmosphere by the means of expression is less of a manual work. This paper detects the faces, extract the features as well classify them into different categories which ultimately lead to expression recognition. We evaluate our proposed method with the dataset which we used and the recall of angry, fear, happy, neutral, sad, and surprise is 60%, 31%, 84%, 22%, 57% and 58% respectively and the f1-score is 51% 35%, 82%, 25%, 51% and 64% respectively. Experimental results demonstrate the competitive classification of our proposed system.
Cited by 1
Vaibhav Govindwar, Aman Akbani, Aachal Nandeshwar · International Journal of Scientific Research in Computer Science, Engineering and Information Technology · 2024
Related research
- Assessment of Global Solar Radiation at Selected Points in Nigeria Using Artificial Neural Network Model (ANNM) — shares topic coverage
- Technologies in Texture Analysis – A Review — shares topic coverage
- Evaluation of Plastic Waste Classification Systems — shares topic coverage
- Spectral Analysis of Nonlinear Operators: Theory and Applications to Neural Networks and Optimization — shares topic coverage
- Application of Neural Networks for Predicting the Workability of Self-Compacting Concrete — shares topic coverage
Article metrics
Real usage data collected on this platform.
5
Page views
0
PDF downloads
0
Outbound clicks
1
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.