scholarly journals New Fast Principal Component Analysis for Face Detection

2007 ◽  
Vol 11 (2) ◽  
pp. 195-201 ◽  
Author(s):  
Hazem M. El-Bakry ◽  
Keyword(s):  
Principal Component Analysis ◽  
Face Detection ◽  
Cross Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Pattern Detection ◽  
Face Detection And Recognition ◽  
Real Time Applications ◽  
Detection And Recognition

Principal component analysis (PCA) has different important applications, especially in pattern detection such as face detection and recognition. In real-time applications, response time must be as fast as possible. For this, we propose a new PCA implementation for fast face detection based on the cross-correlation in the frequency domain between the input image and eigenvectors (weights). Simulation results demonstrate that our proposal is faster than the conventional one, and experimental results for different images show good performance.

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

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.

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