Human face detection based on skin color with principal component analysis (PCA) algorithm

2019 ◽  
Author(s):  
Dorsa Kiaei ◽  
Saeed Tavakoli
Keyword(s):  
Principal Component Analysis ◽  
Face Detection ◽  
Skin Color ◽  
Principal Component ◽  
Component Analysis ◽  
Human Face ◽  
Human Face Detection ◽  
Pca Algorithm

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.

MATLAB Implementation of Face Identification Using Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5402-5408
Author(s):  
Nasrul Humaimi Mahmood ◽  
Ismail Ariffin ◽  
Camallil Omar ◽  
Nur Sufiah Jaafar
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Face Identification ◽  
Human Face ◽  
Face Images ◽  
The Face ◽  
Matlab Implementation ◽  
Pca Algorithm

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.

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 Robust Procedure for Damage Detection from Strain Measurements Based on Principal Component Analysis

2013 ◽  
Vol 558 ◽  
pp. 128-138 ◽  
Author(s):  
Alfredo Guemes ◽  
J. Sierra-Pérez ◽  
J. Rodellar ◽  
L. Mujica
Keyword(s):  
Principal Component Analysis ◽  
Multivariable Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Complex Data ◽  
Data Set ◽  
Strain Measurements ◽  
Before And After ◽  
Small Cracks ◽  
Pca Algorithm

FBGs are excellent strain sensors, because of its low size and multiplexing capability. Tens to hundred of sensors may be embedded into a structure, as it has already been demonstrated. Nevertheless, they only afford strain measurements at local points, so unless the damage affects the strain readings in a distinguishable manner, damage will go undetected. This paper show the experimental results obtained on the wing of a UAV, instrumented with 32 FBGs, before and after small damages were introduced. The PCA algorithm was able to distinguish the damage cases, even for small cracks. Principal Component Analysis (PCA) is a technique of multivariable analysis to reduce a complex data set to a lower dimension and reveal some hidden patterns that underlie.

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