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Related Experiment Video

Updated: Jun 27, 2025

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Texture based feature extraction using symbol patterns for facial expression recognition.

Mukku Nisanth Kartheek^1,2, Munaga V N K Prasad¹, Raju Bhukya²

¹Institute for Development and Research in Banking Technology, Hyderabad, India.

Cognitive Neurodynamics

|May 3, 2024

Summary

This summary is machine-generated.

New Symbol Patterns (SP) improve facial expression recognition (FER) by extracting texture-based features. These novel methods show enhanced accuracy across multiple datasets compared to existing techniques.

Keywords:

Appearance based features Facial expression recognition Feature descriptors Symbol patterns Texture based features

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Area of Science:

Computer Vision
Artificial Intelligence
Human-Computer Interaction

Background:

Facial expressions are crucial for human social interaction and emotion conveyance.
Effective feature extraction is vital for high-performing automatic Facial Expression Recognition (FER) systems.

Purpose of the Study:

To propose novel texture-based feature descriptors for facial feature extraction in FER systems.
To evaluate the performance of these new descriptors against existing methods.

Main Methods:

Introduction of three Symbol Patterns (SP1, SP2, SP3) inspired by the Swastik symbol for facial feature extraction.
SP1 uses a 3x3 neighborhood, while SP2 and SP3 utilize 5x5 neighborhoods with varying pixel comparisons.
Evaluation of SP methods with diverse weighting schemes (natural, Fibonacci, odd, prime, squares, binary) on multiple benchmark datasets (MUG, TFEID, CK+, KDEF, FER2013, FERG).

Main Results:

The proposed Symbol Patterns demonstrated improved recognition accuracy in FER tasks.
Experimental analysis confirmed the effectiveness of SP methods across various datasets.
Comparison with existing FER techniques showed a noticeable enhancement in recognition performance.

Conclusions:

The novel Symbol Patterns offer a promising approach for enhancing facial feature extraction in FER systems.
The proposed methods provide a valuable contribution to the field of automatic emotion recognition.
Further research can explore variations and applications of these texture-based descriptors.