scholarly journals An Optimal MSER Descriptor Based Facial Expression Recognition System using Artificial Intelligence Method

2020 ◽  
Vol 9 (3) ◽  
pp. 3571-3574
Keyword(s):  
Artificial Intelligence ◽  
Face Detection ◽  
Skin Color ◽  
Recognition System ◽  
Expression Recognition ◽  
Binary Images ◽  
Knowledge Based ◽  
Average Accuracy ◽  
Feature Based ◽  
Image Computation

In this Artificial intelligence based Facial emotion recognition system (AI_FERS) model, emotions of facial expressions through performing some predefined steps such as face acquisition, pre-processing of images, face detection, feature extraction & classification have recognized. In the pre-processing of the image phase include the approaches used for face detection is: Knowledge-based, Feature-based, Template-based, and Appearance-based approach. Binary image computation, Skin-color segmentation and morphological filtering, which includes the dilation of Binary images and Gray Images are being extensively applied. For features extraction from images MSER (Maximally Stable External Regions) technique is used. At the final step categorize of emotion into six parts: surprise, fear, disgust, anger, happiness, and sadness come as an outcome using ANN (Artificial Neural Network) technique. The efficiency of the system is examined based on performance parameters such as FAR, FRR, accuracy and execution time. The average accuracy of the AI_FERS model examined is about 98.23 %.

Research of Face Detection Method Based on Skin Color Feature

2013 ◽  
Vol 373-375 ◽  
pp. 478-482
Author(s):  
Qing Ye
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Recognition System ◽  
Human Face ◽  
Color Feature ◽  
Binary Images ◽  
Face Recognition System ◽  
The Face ◽  
To Receive

Human face detection is the first critical step of face recognition system. This paper proposed a face detection method based on skin color feature. Firstly, the method of building a skin color feature from RGB to YCbCr and extracting skin color region according the chrominance similarity was used to extract the face gray image. Secondly, image smoothness and image binarization were used to receive the binary image, then mathematical morphology operators were used to eliminate the binary images noise and disturbance. At last, human face regions are detected through projection operation. The result of experimentation affirms that the method is efficient to detect human face.

scholarly journals Face Recognition System for Indoor Applications Based on Video with the LNMF and NMFsc Methods

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Della Gressinda Wahana ◽  
Bambang Hidayat ◽  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Matrix Factorization ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Face Recognition System ◽  
Average Accuracy ◽  
Non Negative Matrix Factorization

Face detection and face recognition are among the most important research topics in computer vision, as many applications use faces as objects of biometric technology. One of the main issues in applying face recognition is recording the attendance of active participants in a room. The challenge is that recognition through video with multiple object conditions in one frame may be difficult to perform. The Principal Component Analysis method is commonly used in face detection. Principal Component Analysis still has shortcomings: the accuracy decreases when it is applied to large datasets and performs slowly in real-time applications. Therefore, this study simulates a face recognition system installed in a room based on video recordings using Non-negative Matrix Factorization suppressed carrier and Local Non-negative Matrix Factorization methods. Data acquisition is obtained by capturing video in classrooms with a resolution of 640 x 480 pixels in RGB, .avi format, video frame rate of 30 fps, and video duration of ±10 seconds. The proposed system can perform face recognition in which the average accuracy value of the Local Non-negative Matrix Factorization method is 71.61% with a computation time of 152,634 seconds. By contrast, the average accuracy value of the Non-negative Matrix Factorization suppressed carrier method is 86.76% with a computation time of 467,785 seconds. The proposed system is expected to be used for simultaneously finding and identifying faces in real time.

A Face Based Real Time Communication for Physically and Speech Disabled People

2013 ◽  
pp. 1434-1460
Author(s):  
Ong Chin Ann ◽  
Marlene Valerie Lu ◽  
Lau Bee Theng
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Time Behaviour ◽  
The Disabled ◽  
Detection Stage

The main purpose of this research is to enhance the communication of the disabled community. The authors of this chapter propose an enhanced interpersonal-human interaction for people with special needs, especially those with physical and communication disabilities. The proposed model comprises of automated real time behaviour monitoring, designed and implemented with the ubiquitous and affordable concept in mind to suit the underprivileged. In this chapter, the authors present the prototype which encapsulates an automated facial expression recognition system for monitoring the disabled, equipped with a feature to send Short Messaging System (SMS) for notification purposes. The authors adapted the Viola-Jones face detection algorithm at the face detection stage and implemented template matching technique for the expression classification and recognition stage. They tested their model with a few users and achieved satisfactory results. The enhanced real time behaviour monitoring system is an assistive tool to improve the quality of life for the disabled by assisting them anytime and anywhere when needed. They can do their own tasks more independently without constantly being monitored physically or accompanied by their care takers, teachers, or even parents. The rest of this chapter is organized as follows. The background of the facial expression recognition system is reviewed in Section 2. Section 3 is the description and explanations of the conceptual model of facial expression recognition. Evaluation of the proposed system is in Section 4. Results and findings on the testing are laid out in Section 5, and the final section concludes the chapter.

Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

2020 ◽  
Vol 34 (14) ◽  
pp. 2056010
Author(s):  
Lei Huang ◽  
Fei Xie ◽  
Jing Zhao ◽  
Shibin Shen ◽  
Weiran Guang ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Human Emotion ◽  
The Face ◽  
Color Enhancement

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

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