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\title{Performance Enhancement of Face Recognition Algorithms based on Principal Components Analysis}
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\begin{document}

             \author[1]{Elbala  Ahmed}

             \author[2]{Khalil. B. hmed.  A}

             \author[3]{Banaga Hassan  Mohammed}

             \affil[1]{  ALIMAM ALHADI College-Electrical Engineering, Sudan.}

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\date{\small \em Received: 1 January 1970 Accepted: 1 January 1970 Published: 1 January 1970}

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\begin{abstract}
        


In this paper many face recognition algorithms and codes were studied and tested, and it was concluded that they still face the challenge of not providing optimal accuracy and precision, especially in the case of images that have some distortions such as those resulting from poor illumination, different angles of taking the image and different facial expressions or wear hats, masks or glasses. Although recognition technologies using iris and fingerprint are more accurate, face recognition technology is the most common and widely utilized since it is simple to apply and execute, in addition it can be used directly anywhere and does not require any physical input from user. The results show that the best performance of face recognition depends on the number of principal components (PCs), the percentage of face recognition increases in the ranges of 10%, 40%, 50%, 80%, 90% and 100% when the PCs increase in order of 1, 3, 5, 7, 11 and 15, respectively.

\end{abstract}


\keywords{face recognition, PCA, image processing.}

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{{\textit{CrossRef DOI of original article:}} \underline{}}
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\section[{I. Introduction}]{I. Introduction}\par
ace recognition systems (FRS) is a biometric identification mechanism, like other methods such as (fingerprint, voice recognition, iris recognition and handwritten recognition), is shown to be more important both theoretically and practically \hyperref[b0]{[1,}\hyperref[b1]{2]}. Face is a complex multidimensional structure and needs good computing techniques for recognition. To find out exact identity of any person, face recognition is very essential technology. Can recognize a number of faces learned throughout our lifespan and identify that faces at a glance even though that persons became old in age. There may be variations in faces due to aging and distractions like beard, glasses or change of hairstyles \hyperref[b2]{[3,}\hyperref[b3]{4,}\hyperref[b4]{5,}\hyperref[b5]{6]}. Face detection from a single image or sequence of image is a challenging task, because of the variance in size, orientation, color, expression, occlusion, and luminance of image, to build a fully automated system that extracts information from images of human faces, it is essential to develop efficient algorithms to detect human faces. The primary objective of facial discovery algorithms is to determine whether there is any face in the picture or not. Recently, a lot of the studies work in facial recognition and facial detection has been suggested to make it more progressive and accurate, but it is revolutionizing in this area when a realtime facial detector, able to discover faces in real-time accurately \hyperref[b6]{[7]}. 
\section[{II. Proposed System}]{II. Proposed System}\par
The proposed system of this paper were based on the tries to recognize the input image by matching it with existing images (data base), by selecting the stage of image acquisition (Acquired), extracting the face image from the total image (Detection), aligning stage and image standardization (adjusting the angle of the face By camera angle) (Alignment), extraction of important features of the image (Extract), The stage of matching between the desired image and the image store (Matching) and The stage of issuing the report is closer to the image or no report (Report), this steps illustrated in figure \hyperref[fig_0]{1}. Biometric approaches aim to identify an individual by his unique physical characteristics and biological traits. Given these problems, the development of biometrics approaches such as face recognition, fingerprint, and voice recognition proves to be a superior solution for identifying individuals over that of PIN codes. Using of biometric techniques not only uniquely identifies an individual, but also minimizes the risk of someone else using the unauthorized identity. 
\section[{III. Methodology}]{III. Methodology}\par
Our work aims to improve the performance enhancement of the face recognition algorithm using PCA by increasing the number of PCs including one dimensional value for face recognition. Experiments were carried out using MATLAB. The investigation was used to adjust the best number of images for each individual to be used in the training set, that gives the highest percentage of recognition. the highest matching ratio was made by multiples of images in the training set for each person. In this experiment, the number of PCs in the test database was increased by ten images per person in the training database as provided by the experiment. We change the PCs, trying to decide the best matching. PCA flow chart for feature extraction process can be seen in figure \hyperref[b1]{(2)}.       
\section[{V. Discussion}]{V. Discussion}\par
Increasing the number of images for each person in the training set were get best recognition rate, by comparing the results of the experiments, the enhanced algorithm gives high recognition ratio when the PCs were increased. 
\section[{VI. Conclusion}]{VI. Conclusion}\par
This paper discusses how to augment the PCA feature with the selected optimization method by increasing the PCs to improve the accuracy of face recognition models. Enhancement is one of the most useful tools that can be used in image processing and, in particular, in areas such as object matching. This paper aims to optimize the face recognition using the PCA algorithm, by increasing the PCs and number of images in the training set. Our enhanced algorithm reduces the participated eigenvectors in the algorithm to reduce the computation time. Increasing the number of images for each person in the training set to get the best recognition rate causes a long computational time, which increased exponentially with the database size. By comparing the results of the experiments the improved algorithm gives a reduction of the recognition ratio when the PCs is smaller, while the enhanced algorithm shows noticeable improvement and gives considerable increase of the recognition ratio when increasing the PCs. Future work will focus on success and increasing the face recognition rate for huge databases. To improve the results, the algorithms for face recognition could be upgraded to detect multiple faces in the same image. We will try to develop a system using a video camera that will work with real-time face recognition.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{475}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 4 := 7 ©Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-7.png}
\caption{\label{fig_5}Figure 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
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Year 2023\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
22\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume Xx XIII Issue II V ersion I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
( ) F\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Researches in Engineering\tabcellsep 1 2\tabcellsep 1 3\tabcellsep 1 4\tabcellsep 10 40\\
\tabcellsep 3\tabcellsep 5\tabcellsep 5\tabcellsep 50\\
\tabcellsep 4\tabcellsep 7\tabcellsep 8\tabcellsep 80\\
\tabcellsep 5\tabcellsep 11\tabcellsep 9\tabcellsep 90\\
\tabcellsep 6\tabcellsep 15\tabcellsep 10\tabcellsep 100\\
© 2023 Global Journ als\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 			\label{foot_0}\footnote{\label{foot_0} © 2023 Global Journ als} 		 		\backmatter  			 \par
Year 2023			 			  				\begin{bibitemlist}{1}
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\bibitem[Tran et al. ()]{b5}\label{b5} 	 		‘Disentangled representation learning gan for pose-invariant face recognition’.  		 			Luan Tran 		,  		 			Xi Yin 		,  		 			Xiaoming Liu 		.  	 	 		\textit{Proceedings of the IEEE conference on computer vision and pattern recognition},  		 (the IEEE conference on computer vision and pattern recognition)  		2017.  	 
\bibitem[Maia and Trindade ()]{b1}\label{b1} 	 		‘Face detection and recognition in color images under MATLAB’.  		 			Deise Maia 		,  		 			Roque Trindade 		.  	 	 		\textit{International Journal of Signal Processing}  		2016. 9  (2) .  	 	 (Image Processing and Pattern Recognition) 
\bibitem[Barnouti and Hazim ()]{b4}\label{b4} 	 		‘Face detection and recognition using Viola-Jones with PCA-LDA and square euclidean distance’.  		 			Nawaf Barnouti 		,  		 			Hazim 		.  	 	 		\textit{International Journal of Advanced Computer Science and Applications (IJACSA)}  		2016. 7  (5) .  	 
\bibitem[Kumar et al. ()]{b0}\label{b0} 	 		‘Face detection techniques: a review’.  		 			Ashu Kumar 		,  		 			Amandeep Kaur 		,  		 			Munish Kumar 		.  	 	 		\textit{Artificial Intelligence Review}  		2019. 52.  	 
\bibitem[Mahmud ()]{b3}\label{b3} 	 		‘Human face recognition using PCA based Genetic Algorithm’.  		 			Firoz Mahmud 		.  	 	 		\textit{2014 International Conference on Electrical Engineering and Information \& Communication Technology},  		2014. IEEE.  	 
\bibitem[Li et al. ()]{b6}\label{b6} 	 		‘On low resolution face recognition in the wild: Comparisons and new techniques’.  		 			Pei Li 		,  		 			Loreto Prieto 		,  		 			Domingo Mery 		,  		 			Patrick J Flynn 		.  	 	 		\textit{IEEE Transactions on Information Forensics and Security}  		2019. 14  (8)  p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
