scholarly journals HYBRID APPROACH FOR HUMAN FACIAL EXPRESSION DETECTION THROUGH TLBO AND PFEF

2020 ◽  
Vol 5 (2) ◽  
pp. 328-333
Author(s):  
Samta Jain Goya ◽  
Dr. Arvind K. Upadhyay ◽  
Dr. R. S. Jadon ◽  
Rajeev Goyal
Keyword(s):  
Facial Expression ◽  
Skin Color ◽  
Detection Method ◽  
Hybrid Approach ◽  
Human Face ◽  
Related Activity ◽  
Tlbo Algorithm ◽  
Human Facial Expression ◽  
Shape And Size ◽  
Color Texture

This paper introduces facial expression detection method which is based on facial’s selected feature and optimized those selected features. The study says that human face generally faced generally consist of skin color, texture shape and size of face in this paper we study skin color and texture of human face .This process consist two steps for the same. In first known as detection of expression which uses PFEF (partial feature extension function) and in second, for optimization we used TLBO algorithm is basically a population base searching technics. Also uses soft computation technics because we cannot actual and accurate for human related activity. Varieties of technic are used for the same purpose this as per use hybrid approach to get better result.

Somatic Semiotics: Emotion and the Human Face in the Sagas andÞættirof Icelanders

Traditio ◽  
2014 ◽  
Vol 69 ◽  
pp. 125-145
Author(s):  
Kirsten Wolf
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Internal State ◽  
Old Norse ◽  
Human Face ◽  
Affective States ◽  
Mixed Emotions ◽  
Wide Range ◽  
Point Of Departure ◽  
Human Facial Expression

The human face has the capacity to generate expressions associated with a wide range of affective states. Despite the fact that there are few words to describe human facial behaviors, the facial muscles allow for more than a thousand different facial appearances. Some examples of feelings that can be expressed are anger, concentration, contempt, excitement, nervousness, and surprise. Regardless of culture or language, the same expressions are associated with the same emotions and vary only in intensity. Using modern psychological analyses as a point of departure, this essay examines descriptions of human facial expressions as well as such bodily “symptoms” as flushing, turning pale, and weeping in Old Norse-Icelandic literature. The aim is to analyze the manner in which facial signs are used as a means of non-verbal communication to convey the impression of an individual's internal state to observers. More specifically, this essay seeks to determine when and why characters in these works are described as expressing particular facial emotions and, especially, the range of emotions expressed. The Sagas andþættirof Icelanders are in the forefront of the analysis and yield well over one hundred references to human facial expression and color. The examples show that through gaze, smiling, weeping, brows that are raised or knitted, and coloration, the Sagas andþættirof Icelanders tell of happiness or amusement, pleasant and unpleasant surprise, fear, anger, rage, sadness, interest, concern, and even mixed emotions for which language has no words. The Sagas andþættirof Icelanders may be reticent in talking about emotions and poor in emotional vocabulary, but this poverty is compensated for by making facial expressions signifiers of emotion. This essay makes clear that the works are less emotionally barren than often supposed. It also shows that our understanding of Old Norse-Icelandic “somatic semiotics” may well depend on the universality of facial expressions and that culture-specific “display rules” or “elicitors” are virtually nonexistent.

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.

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 Human Face Detection in Color Images

2021 ◽  
Author(s):  
Jun Gao
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Video Retrieval ◽  
Color Images ◽  
Human Face ◽  
Detection Scheme ◽  
Retrieval Systems ◽  
Human Face Detection ◽  
The Face ◽  
Video Retrieval Systems

Detection of human face has many realistic and important applications such as human and computer interface, face recognition, face image database management, security access control systems and content-based indexing video retrieval systems. In this report a face detection scheme will be presented. The scheme is designed to operate on color images. In the first stage of algorithm, the skin color regions are detected based on the chrominance information. A color segmentation stage is then employed to make skin color regions to be divided into smaller regions which have homogenous color. Then, we use the iterative luminance segmentation to further separate the detected skin region from other skin-colored objects such as hair, clothes, and wood, based on the high variance of the luminance component in the neighborhood of edges of objects. Post-processing is applied to determine whether skin color regions fit the face constrains on density of skin, size, shape and symmetry and contain the facial features such as eyes and mouths. Experimental results show that the algorithm is robust and is capable of detecting multiple faces in the presence of a complex background which contains the color similar to the skin tone.

scholarly journals A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162702 ◽  
Author(s):  
Muhammad Hameed Siddiqi ◽  
Md. Golam Rabiul Alam ◽  
Choong Seon Hong ◽  
Adil Mehmood Khan ◽  
Hyunseung Choo
Keyword(s):  
Facial Expression ◽  
Markov Model ◽  
Maximum Entropy ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Facial Expression

scholarly journals Transmitting and Decoding Facial Expressions

2005 ◽  
Vol 16 (3) ◽  
pp. 184-189 ◽  
Author(s):  
Marie L. Smith ◽  
Garrison W. Cottrell ◽  
FrédéAric Gosselin ◽  
Philippe G. Schyns
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Brain Structures ◽  
Human Face ◽  
The Face ◽  
The Brain

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

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