• Corpus ID: 18875505

Classification of Human Facial Expression based on Mouth Feature using SUSAN Edge Operator

@inproceedings{Prasad2014ClassificationOH,
  title={Classification of Human Facial Expression based on Mouth Feature using SUSAN Edge Operator},
  author={M. Guru Prasad and Ajit Danti},
  year={2014}
}
In this paper, human facial expressions are recognized based on the mouth feature using Susan edge detector (3,4). Face part is segmented from the face image, in which mouth feature is separated and potential geometrical features are used for the determination of facial expression such as surprise, neutral, sad and happy. Experimentation is done on standard JAFFE database (8) images of different people and efficacy of the results are discussed. 

Figures and Tables from this paper

Segmentation of Facial Features Based on Human Face Expressions
TLDR
This work proposes a method to extract mouth and eye from an given input facial image by applying the appropriate threshold value to the given image by using statistical features such as operations like right array division and right array multiplication.
Human Emotion classification based on Eyes and Mouth using Susan Edges
TLDR
Instead of considering the features of the whole face, only eyes and mouth are considered for human emotion classification such as surprise, neutral, sad, happy and Anger based on the Susan edge operator.
JAFFE Human Face Expressions
TLDR
Mouth and eye are extracted by applying the appropriate threshold value to the given image by using statistical features such as operations like right array division and right array multiplication and in terms of accuracy the authors obtained better performance in the proposed method.
A NOVEL IMAGE PROCESSING TECHNIQUE TO EXTRACT FACIAL EXPRESSIONS FROM MOUTH REGIONS
TLDR
An image processing technique to recognize various facial expressions from mouth regions is proposed and is executed on various emotional images (natural, joy, angry, surprise) of two different persons.
Subject Independent Facial Emotion Classification Using Geometric Based Features
TLDR
The results of conducted tests indicate that the use of suggested distances, angles and others relative geometric features for recognition give accuracy about 95.73% when the seven emotion classes are tested and 97.23%" when the 6 classes are only tested.
Facial Emotion Detection Based on the Features of Mouth Regions
TLDR
The main aim of this research work is to propose a technique to detect the facial emotion from the features of the mouth region using Edge detection, region filling, and morphological algorithms to extract the lips and filled mouth region.
Image Processing Techniques To Recognize Facial Emotions
TLDR
The goal of the proposed work is to build an emotion recognition system that includes face detection, non-skin region extraction and morphological processing finally, emotion recognition.

References

SHOWING 1-10 OF 22 REFERENCES
Automatic facial feature extraction and expression recognition based on neural network
TLDR
Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry for face portion segmentation and localization.
Analysis of Facial Expression using Gabor and SVM
TLDR
This paper presents a method to analyze facial expression from images by applying Gabor wavelet filter banks on face images to reduce the computational complexity.
A Study of Techniques for Facial Detection and Expression Classification
TLDR
Facial expression recognition is analyzed with various methods of facial detection, facial feature extraction and classification to derive holistic and feature based facial recognition approaches.
Analysis of Facial Expression using Gabor and
TLDR
This paper presents a method to analyze facial expression from images by applying Gabor wavelet filter banks on face images to reduce the computational complexity.
Local features based facial expression recognition with face registration errors
TLDR
Overall LBP with overlapping gives the best performance (92.9% recognition rate on the Cohn-Kanade database), while maintaining a compact feature vector and best robustness against face registration errors.
FACIAL EXPRESSION RECOGNITION SYSTEM USING WEIGHT-BASED APPROACH
TLDR
A method to identify the facial expressions of a user by processing images taken from a webcam by implementing the concept of Haar-like features to prioritize reduction in detection time over accuracy.
The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature
In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and
Feature Extraction based on Local Directional Pattern with SVM Decision-level Fusion for Facial Expression Recognition
TLDR
A novel facial expression recognition method based on global and local features extraction and facial recognition using decision-level fusion is presented and extensive experimental results indicate the effectiveness.
Automatic Analysis of Facial Expressions: The State of the Art
TLDR
The capability of the human visual system with respect to these problems is discussed, and it is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
Facial expression recognition based on Local Binary Patterns: A comprehensive study
...
1
2
3
...