Human Face Classification using Genetic Algorithm

Tania Akter,Md Mijanur

doi:10.14569/ijacsa.2016.070944

Abstract

The paper presents a precise scheme for the development of a human face classification system based human emotion using the genetic algorithm (GA). The main focus is to detect the human face and its facial features and classify the human face based on emotion, but not the interest of face recognition. This research proposed to combine the genetic algorithm and neural network (GANN) for classification approach. There are two way for combining genetic algorithm and neural networks, such as supportive approach and collaborative approach. This research proposed the supportive approach to developing an emotion-based classification system. The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions, such as, mouth (or lip), nose, and eyes. By the analysis of human face, it is seen that most of the emotional changes of the face occurs on eyes and lip. Therefore, two facial feature regions (such as lip and eyes) have been used for emotion-based classification. The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features. To justify the effectiveness of the system, several images were tested. The achievement of this research is higher accuracy rate (about 96.42%) for human frontal face classification based on emotion.

Highlights

The human face plays a central role in social interaction; it is not surprising that automatic facial information processing is an important and highly active subfield of pattern recognition research [1]
Due to the complexity of face recognition, detecting a human face and its facial features and classify the human face base on emotion without identifying the person is of interest [2]
Nagarajan and Sazali Yaacob proposed a method of Genetic Algorithm and Neural Network for Face Emotion Recognition [3].This research focus on finding, segmenting and classifying human faces, includes three parts: human face detection, facial feature segmentations and classification

Summary

Introduction

The human face plays a central role in social interaction; it is not surprising that automatic facial information processing is an important and highly active subfield of pattern recognition research [1]. Knearest neighbor rule was uses with an accuracy of 80% with happy, anger, disgust, surprise emotions [5].Yacoob proposed the same method instead of muscle action, he uses the edge of mouth, eyes and eyebrows, into a frame, mid-level representation, classify the emotions [6]. Black et al proposed a parametric model In this model to extract the shape and movement of eyes, mouth, eyebrows, into a mid and highlevel representation of facial expression with 80% of accuracy [6]. Nagarajan and Sazali Yaacob proposed a method of Genetic Algorithm and Neural Network for Face Emotion Recognition [3].This research focus on finding, segmenting and classifying human faces, includes three parts: human face detection, facial feature segmentations and classification. Classify face image base on Emotion by Neural Network

Methods

Results

Conclusion