scholarly journals The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

i-Perception ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 204166952096112
Author(s):  
Jose A. Diego-Mas ◽  
Felix Fuentes-Hurtado ◽  
Valery Naranjo ◽  
Mariano Alcañiz
Keyword(s):  
Principal Component Analysis ◽  
Personality Traits ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Facial Features ◽  
Direct Link ◽  
Facial Information ◽  
Feature Based ◽  
Human Faces

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.

scholarly journals POTRET KESEJAHTERAAN RAKYAT DI PROVINSI BALI MENGGUNAKAN METODE CHERNOFF FACES

2013 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
I WAYAN WIDHI DIRGANTARA ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Principal Component Analysis ◽  
Graphical Method ◽  
Principal Component ◽  
Component Analysis ◽  
Facial Features ◽  
The People ◽  
Visualization Techniques

Chernoff Faces method is a graphical method of visualization techniques to present data with many variables in the form of a cartoon face which can be determined by 20 parameters or less. In this research it was shown how the Chernoff Faces method was used to see welfare of the people in the province of Bali and Bali's nine regencies. To pair the variables and Chernoff’s facial features, then we used  Principal Component Analysis and survey to make the faces look more human. The result from 18 indicators of welfare of the people in the province of Bali, only 8 indicators were not really well. It was obtained too that Tabanan was the most prosperous regency and Karangasem was the lest prosperous regency.

HUMAN FACE DETECTION USING SILHOUETTES

1995 ◽  
Vol 09 (06) ◽  
pp. 845-867 ◽  
Author(s):  
ASHOK SAMAL ◽  
PRASANA A. IYENGAR
Keyword(s):  
Principal Component Analysis ◽  
Face Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Human Face ◽  
Secure Systems ◽  
Human Face Detection ◽  
Intensity Images ◽  
Human Faces

Face detection is integral to any automatic face recognition system. The goal of this research is to develop a system that performs the task of human face detection automatically in a scene. A system to correctly locate and identify human faces will find several applications, some examples are criminal identification and authentication in secure systems. This work presents a new approach based on principal component analysis. Face silhouettes instead of intensity images are used for this research. It results in reduction in both space and processing time. A set of basis face silhouettes are obtained using principal component analysis. These are then used with a Hough-like technique to detect faces. The results show that the approach is robust, accurate and reasonably fast.

Influence of the Intra-Modal Facial Information for an Identification Approach

2014 ◽  
pp. 318-342
Author(s):  
Carlos M. Travieso ◽  
Marcos del Pozo-Baños ◽  
Jaime R. Ticay-Rivas ◽  
Jesús B. Alonso
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Identification System ◽  
Facial Features ◽  
Classifier Systems ◽  
Lighting Conditions ◽  
Facial Information ◽  
Identification Approach

This chapter presents a comprehensive study on the influence of the intra-modal facial information for an identification approach. It was developed and implemented a biometric identification system by merging different intra-multimodal facial features: mouth, eyes, and nose. The Principal Component Analysis, Independent Component Analysis, and Discrete Cosine Transform were used as feature extractors. Support Vector Machines were implemented as classifier systems. The recognition rates obtained by multimodal fusion of three facial features has reached values above 97% in each of the databases used, confirming that the system is adaptive to images from different sources, sizes, lighting conditions, etc. Even though a good response has been shown when the three facial traits were merged, an acceptable performance has been shown when merging only two facial features. Therefore, the system is robust against problems in one isolate sensor or occlusion in any biometric trait. In this case, the success rate achieved was over 92%.

scholarly journals A Hybrid Enhanced Real-Time Face Recognition Model using Machine Learning Method with Dimension Reduction

2021 ◽  
Vol 1 (3) ◽  
pp. 12-16
Author(s):  
Jaya Kumari ◽  
◽  
Kailash Patidar ◽  
Mr. Gourav Saxena ◽  
Mr. Rishi Kushwaha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Technology Implementation ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Experimental Result ◽  
Scale Invariant ◽  
Recognition Model

Face recognition techniques play a crucial role in numerous disciplines of data security, verification, and authentication. The face recognition algorithm selects a face attribute from an image datasets. Recognize identification is an authentication device for verification as well as validation having both data analysis and feasible significance. The facerecognizing centered authentication framework can further be considered an AI technology implementation for instantly identifying a particular image. In this research, we are presenting a hybrid face recognition model (HFRM) using machine learning methods with “Speed Up Robust Features” (SURF), “scale-invariant feature transform” (SIFT), Locality Preserving Projections (LPP) &Principal component analysis (PCA) method. In the proposed HFRM model SURF method mainly detects the local feature efficiently. SIFT method mainly utilizes to detect the local features and recognize them. LPP retains the local framework of facial feature area which is generally quite meaningful than on the sequence kept by a 'principal component analysis (PCA) as well as “linear discriminate analysis” (LDA). The proposed HFRM method is compared with the existing (H. Zaaraoui et al., 2020) method and the experimental result clearly shows the outstanding performance in terms of detection rate and accuracy % over existing methods.

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