This document describes a face detection method using principal component analysis. It first preprocesses images using histogram equalization to address illumination issues. It then detects faces using skin segmentation to identify skin regions. Finally, it recognizes the extracted facial features using principal component analysis and a neural network, which reduces the dimensionality of the images for efficient recognition.

1. http://www.iaeme.com/IJCET/index.asp 174 editor@iaeme.com
International Journal of Computer Engineering & Technology (IJCET)
Volume 7, Issue 3, May-June 2016, pp. 174–178, Article ID: IJCET_07_03_016
Available online at
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=7&IType=3
Journal Impact Factor (2016): 9.3590 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6367 and ISSN Online: 0976–6375
© IAEME Publication
FACE DETECTION USING PRINCIPAL
COMPONENT ANALYSIS
Parteek Kumar
M.Tech. CSE
PPIMT Hisar, Haryana, India
Praveen Sehgal
HOD, CSE
PPIMT Hisar, Haryana, India
ABSTRACT
This paper describes for a robust face recognition system using skin
segmentation technique. This paper addresses the problem of detecting faces
in color images in the presence of various lighting conditions. In this paper
the face is preprocessed using histogram equalization to avoid illumination
problems and then is detected using skin segmentation method. The principal
component analysis using neural network is used to recognize the extracted
facial features.
Key words: Histogram Equalization, Principal Component Analysis
Cite this Article: Parteek Kumar and Praveen Sehgal, Face Detection Using
Principal Component Analysis, International Journal of Computer
Engineering and Technology, 7(3), 2016, pp. 174–178.
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=7&IType=3
1. INTRODUCTION
A facial recognition system is an application that uses image processing and is used
for identification and verification of a person in an image or a video frame. It is not
possible for human to capture all the faces in his mind and recognize them. Face
detection and recognition has a number of applications nowadays. Some of them are
listed below:
1. Virtual reality
2. Database recovery
3. Multimedia
4. Computer entertainment
5. Information security
6. Biometric applications

2. Face Detection Using Principal Component Analysis
http://www.iaeme.com/IJCET/index.asp 175 editor@iaeme.com
7. Home video surveillance system.
In this paper we will use face recognition system for both identification and
verification using principle component analysis.
Advantages and Disadvantages of PCA
The key advantages of PCA are listed below:
1. Recognition with PCA method is simple and efficient as compared to other
approaches.
2. Data compression is achieved.
3. No knowledge of geometry of face is required [5].
4. The key disadvantages of PCA are:
5. The covariance matrix is difficult to solve accurately and hence is time consuming.[5]
6. The size and location of each face image must remain similar. [7]
7. The method is very sensitive to scale.
2. LITERATURE SURVEY
Face recognition is one of those challenging problems and till now, there is no
technique that provides a robust solution to all situations. Several algorithms and
techniques for face recognition have been developed in the past by researchers. Some
of them are discussed briefly in this section. The problem of automatic face
recognition involves three key steps/subtasks:
1. Detection of faces
2. Feature extraction
3. Identification and/or verification.
Sometimes, different subtasks can be carried out simultaneously. For example, the
facial features (eyes, nose, mouth) extraction step can be performed with the face
detection. A popular and robust face detection algorithm uses an object detector
developed at MIT by Viola and Jones [1]. The detector uses a cascade of boosted
classifiers working with Haar-like features to decide whether a region of an image is a
face. On web blogspot [2] present a face recognition system that attempts to recognize
faces using skin segmentation technique. Yanjiang Wang et al. [3] proposed a fast
face detection method in color images under complex background. The method firstly
calculates similar pixel color image with the person's skin color clustering and region
segmentation, and then use the face features of the wavelet decomposition analysis for
each candidate area, if the detection is similar to the characteristics of the regional
distribution of the face with one of the predefined model, the regional representative
of the face. In [4], Aamer S.S.Mohamed presented an approach that relies on skin
based color, while features can be extracted from two dimensional Discrete Cosine
transform and neural networks which used to detect faces by using skin color from
DCT coefficient of Cb and Cr features vectors.
3. METHODOLOGY
The previous section illustrated different techniques and methods of face detection
and recognition. Each category of method performs well in certain criteria and also
has drawbacks as well. Systems with robustness and certain level of accuracy are still
far away. Keeping in view our case study the following architecture is proposed for
the detection and recognition system.

3. Parteek Kumar and Praveen Sehgal
http://www.iaeme.com/IJCET/index.asp 176 editor@iaeme.com
The image is to be preprocessed by applying histogram equalization to avoid
illumination problem. As there are three steps in face recognition system, first the face
will be detected (if there is a face in the image or video) by skin segmentation
technique. A range is specified for the chrominance component of an image. Based on
this specified range, the system is able to differentiate between skin color and the
background. This gives face on the image.
For second step i.e. facial features extraction, the following steps will be performed:
1. Acquire image
2. Apply transform i.e. Haar Transform
3. Threshold image
4. Extract details of image
5. Scanning
6. Extract features
Then after extracting the features, the face has to be recognized that is the third
step. For face recognition we will be using a holistic matching approach i.e. PCA
using neural method. To perform PCA several steps will be performed:
Figure 1 Steps to perform PCA
The eigen object recognizer class performs all of this and then feeds the
transposed data as a training set into a Neural Network. When it is passed to recognize
it performs PCA and compares the generated Eigen values and Eigenvectors to the
ones from the training set .The Neural Network then produces a match if one has been
found or a negative match if no match is found.
4. EXPERIMENTAL ANALYSIS
First of all we have performed face detection using skin segmentation technique. To
avoid illumination problem, we preprocessed our image using histogram equalization.

4. Face Detection Using Principal Component Analysis
http://www.iaeme.com/IJCET/index.asp 177 editor@iaeme.com
Figure 2 Original Image
The next step is to detect the skin regions in an image.
But because of certain broken blobs they are identified as separate blobs and this
makes it difficult to detect faces with accuracy. We would fill the gaps with white
spaces so as to make it a solid white blob. MATLAB has an inbuilt function for the
same known as ‘imfill’. Before performing ‘imfill’ we need to convert our grayscale
image into binary image. At last a red bounding box has to be put around the detected
face.
Figure 3 Face Detected Image
5. CONCLUSION
This paper presents an efficient approach for face detection of image which handled
illumination issues. For preprocessing, histogram equalization is used and for face
detection skin segmentation technique is used in order to detect faces in an image. In
future it can be used for face recognition with the help of Principal Component
Analysis.
REFERENCES
[1] Paul Viola and Michael Jones, Robust real-time object detection, In International
Journal of Computer Vision, 2001.
[2] http://engineeringprojects101.blogspot.in/2012/07/face-detection-
usingmatlab.html
[3] Yanjiang Wang and Baozong Yuan, A Fast Human Face Detection Method
From Color Images Under Complex Background [J], Acta Electronica Sinica,
2002, 30, 10.

5. Parteek Kumar and Praveen Sehgal
http://www.iaeme.com/IJCET/index.asp 178 editor@iaeme.com
[4] Aamer S.S. Mohamed, Ying Weng, Stan S Ipson, Jianmin Jiang, Face Detection
Based on Skin Color in Image by Neural Networks, IEEE, 2007
[5] P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J.
Marques, J. Min and W. Worek, Overview of the Face Recognition Grand
Challenge, in Computer vision and pattern recognition, 2005. CVPR 2005. IEEE
Computer Society Conference on, 2005, pp. 947–954.
[6] D. Srinivasulu Asadi, Ch. DV Subba Rao and V. Saik-rishna A Comparative
Study of Face Recognition with Principal Component Analysis and Cross-
Correlation Technique, International Journal of Computer Applications Vol. 10,
2010.
[7] S.K. Singh, D. S. Chauhan, M. Vatsa, R. Singh, A Robust Skin Color Based
Face Detection Algorithm, Tamkang Journal of Science and Engineering, 6(4),
pp. 227–234 (2003)
[8] Gunjan Dashore and Dr. V.Cyril Raj, An Efficient Method For Face Recognition
Using Principal Component Analysis(PCA), IJATER, 2(2), March 2012
[9] Taranpreet Singh Ruprah, Face Recognition Based on PCA Algorithm with
Neural Network, International Journal of Computer Science & Informatics
(IJCSI), Vol.- II, Issue-1, 2, December 2013
[10] Mattew Turk and Alex Pentland, Eigenfaces for Recognition, SPIE Vol.1192
IRCVV in (i989), pp:23-32
[11] Nazish, 2001. Face recognition using neural networks. Proc. IEEE INMIC 2001,
pp: 277–281.
[12] T. Chen, W. Yin, X.-S. Zhou, D. Comaniciu, T. S. Huang, Total Variation
Models for Variable Lighting Face Recognition and Uneven Background
Correction, IEEE Transactions on Pattern Analysis and Machine Intelligence,
28(9), 2006, pp.1519–1524
[13] Rein-Lien Hsu, Mohamed Abdel-Mottaleb and Anil K. Jain, “Face Detection in
Color Images, IEEE Transactions on Pattern Analysis and Machine
[14] Ming-Hsuan Yang, David J. Kriegman and Na-rendra Ahuja, Detecting Faces in
Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1),
pp.696–706,January 2002.
[15] Song, J., Chi, Z., and Liu, J. (2006). A robust eye detection method using
combined Binary edge and intensity information. Pattern Recognition,
39(6):1110– 1125. [Song et al.,2006]
[16] Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A. (2003). Face
recognition: A Literature survey. ACM, Computing Surveys, 35(4):399–458.
[Zhaoet al., 2003]
[17] E. Bagherian, R. Wirza, N.I. Udzir, Extract of Facial Feature Point, IJCSNS
International Journal of Computer Science and Network Security, 9 (1), January
2009.
[18] V.Vezhnevets, V.Sazonov A. Andreeva, A Survey on Pixel- Based Skin Color
Detection Techniques, Graphics and Media Laboratory, Moscow State
University, Moscow, Russia.
[19] P. Viola and M. Jones. Robust real-time face detection. International Journal of
Computer Vision, 57(2):137–154, May 2004.
[20] Jyoti Verma, Vineet Richariya, Face Detection And Recognition Model Based
On Skin Colour And Edge Information For Frontal Face Images, International
Journal of Computer Engineering and Technology, 3(3), 2012, pp. 384–393.
[21] A.Hemlata and Mahesh Motwani, Single Frontal Face Detection by Finding
Dark Pixel Group and Comparing Xy-Value of Facial Features, International
Journal of Computer Engineering and Technology, 4(3), 2012, pp. 471 - 481.