Efficient Face Detection using PCA and ANN Techniques
Sachin Shende1 & Rahila Patel2
Department of Computer Science, Nagpur University, Chandrapur, India
E-mail : 1 shende.sachin83@gmail.com & 2 rahila.patel@gmail.com
methods have been proposed, such as face detection in
color images based on the fuzzy theory [7], the
discriminating feature analysis and Support Vector
Machine (SVM) classifier for face detection [8], neural
network-based face detection [12]. Face color
information is an important feature in the face detection.
In reference [11], a latest survey of skin-color modeling
and detection methods was presented. Statistical color
modules with application to skin detection was reported
in reference [10]. The quantized skin color regions for
face detection were given in reference [13]. Eye is
another important feature for face detection and
recognition. For example, a robust method for eye
feature extraction on the color image was reported in
reference [14]. Using optimal Wavelet packets and
radial basis functions for eye detection was introduced
in reference. Face detection has the application in
variety of fields used in the teleconference system,
medical imaging intelligent video surveillance, smart
cards and security based public places such as airport
and railway station.
Abstract – This paper presents a algorithm for rapid and
efficient face detection. We have used PCA and ANN
techniques for the efficient and effective face detection.
The framework for efficient face detection using fusion of
PCA and Artificial neural network is presented. The
purpose of PCA is to reduce the large dimensionality of the
data space (observed variables) to the smaller intrinsic
dimensionality of feature space (independent variables),
which are needed to describe the data economically. The
image features are represented as reduced features space
by using PCA which is a dimensionality reduction
technique. Further these features are given as input to the
ANN for training. We have used multilayer perceptron
network for accomplishing this task.
Keywords:- Face detection, PCA, ANN, face recognition
I.
INTRODUCTION
Human Face detection is the process of identifying
the features of faces to detect the faces on the basis of
the discriminant features. Features of faces are eyes,
ears, eyebrows, nose, lips, hairs, chicks, forehead etc.
Face detection can be carried out using these features of
faces. Face is important part to identify the person. It
can be used as the computer visual application. Face is
the important part of our body by which it is easy to
identify and recognize the person. Face detection is one
of the challenging tasks as there are many issues such as
changes in the appearances of faces, variations in poses,
noise,
distortion
and
illumination
condition.
Complications occur in discriminating the two identical
faces for example in case of twins. There are several
techniques for face detection that exist in the literature.
Principal discriminant analysis (PCA) [1] and Linear
discriminant analysis (LDA) [1] are most commonly
used techniques for face detection. Handsdorff distance
measure for face recognition [2], Elastic Graph
Matching (EGM) [3], eigenspace-based face recognition
[4], a novel hybrid neural and dual eigen spaces
methods for face recognition [5], eigenfaces and
Fisherfaces methods [6]. In order to capture the frontal
face image accurately and timely, many face detection
The other face detection techniques are Adaboost,
fisher technique, float boost etc. Over the past decade,
many approaches to improve the performance of face
detection were proposed.
These approaches are
categorized into two types : 1) Knowledge-based
approach,
and 2) Feature invariant approach.
Knowledge-based methods use a priori rules to carry out
face detection, such as the face is usually symmetrical
with each other eyes. Feature invariant approach
include extraction of features, filtering of images with
respect to size , noise, distortion illumination etc. Face
detection is important considering the new medical
science research such as plastic surgery. It has become
challenging to detect and recognize face many due to
the changes and variation in the face that occurs due to
aging, hormonal changes, emotion changes ,skin color
changes etc. In this paper the different techniques used
in the face detection are explained. Review of recent
face detection method is studied in this paper.
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
III. FACE DETECTION
II. PROPOSED APPROACH
1.Creating Database :- In the first phase we will create
a database i.e the set of images containing faces with
variations in their background.. These database will
contain about 50 images. This images are used in the set
of training and testing set.25 images are used as training
set and remaining 25 images will be used as testing set.
Each image is of equal dimension. We have taken the
images each having dimension 27*18.The total
dimension of all the 50 images will be
27*18*50=24,300.This will increase the memory
requirement and time complexity. So we need to reduce
the dimension of the images. The Fig 2. represent the set
of images containing the faces.
In this paper we have tried to merge two techniques
such as PCA and ANN for efficient face detection work.
The proposed system consists of four modules: creating
database, preprocessing of images ,implementation of
PCA, fusion of PCA and ANN .The Fig.1.shows
architecture of the proposed face detection algorithm.
1. Feature extraction of image database using PCA
2. Training of ANN for the reduced image subspace.
3. Detection of face for a given input image
Fig 2..Set of images containing faces of equal
dimension.
Fig. 1 Architecture of proposed face detection
Algorithm.
2. Image Preprocessing :- Images are required to
preprocess before going for face detection. It includes
filtering of noise from the images, image contrast
normalization and orientation localization. After this
step of processing, image database is prepared for
feature extraction.
A. Image Preprocessing :- Images are required to
preprocess before going for face detection. It includes
filtering of noise from the images, image contrast
normalization and orientation localization. After this
step of processing, image database is prepared for
feature extraction.
B. Feature extraction using PCA:-The PCA algorithm is
implemented for the extraction of image feature and
dimensionality reduction. Let X = {I1, I2,…..IN} be the
set of N images. Each of these images is having
dimension equals to t. In PCA this image space is
converted to another image space with a set Y= { J1,
J2,…..JN} of N images. Now, the dimension of each of
these images is f. There is a transformation of X into Y
such that t > f. Generally, a linear orthogonal transform
v = Wu is used such that the retained variance is
maximized.
Fig.3 Image in the database is preprocessed.
3. Feature extraction using PCA:-The PCA algorithm
is implemented for the extraction of image feature and
dimensionality reduction. The main objective of the
PCA in the proposed approach is the dimension
reduction as it will require much space for the database
thereby increasing the memory requirement and time
complexity.Firstly we have taken the set of 50 images
with the uniform dimension .So each image is of the
dimension 27*18=486.So overall dimension of 50
images will be 27*18*50=24,500.Thus the memory
C. Multi-Layer Perceptron (MLP) Network :-We have
used multilayer perceptron network for training and face
detection .The network consist of input layer, hidden
layer and output layer with tensing transfer function
.The neural network learns the face patterns from the
training data set and applies it for detecting face object
from the query image.
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
requirement for storing the data will be large with such
dimension thus increasing the time complexity.PCA
technique is used to reduce the dimension. Steps used in
PCA are as follows:
dimension of the 25 images of the testing is now
486*25=12150.Fig 6.Represent the reduced dimension
of the database images.
1.Each image is represented in the matrix form. The
figure shows the dimension of the first image stored in
the database. Fig.3 represent the matrix with dimension
of first image is 27*18=486.Each element in the matrix
represent the pixel intensity. The range of pixel intensity
is between 0 to 255.
Fig 6.Represent the reduced dimension of the database
images.
4. Using PCA technique ,the dimension of the 25 images
of total size =27*18*25=12150 is now reduced to
dimension=25*25=625.This is performed by using
mathematical tool in PCA. The same dimension
reduction is done for the images in the testing set. Now
these reduced dimensional images will be used for the
efficient detection of face with reduced memory space
and time complexity The .Fig 6.shows the representation
of matrix whose dimension is reduced from 486*25 to
25*25.This reduced dimensional images is further used
with ANN for the efficient detection of faces.
Fig 4.Matrix representation of Image
2,Conversion of 2D matrix in 1D matrix:-The 2D matrix
with dimension 27*18 is now converted into the single
matrix i.e 486*1.So new matrix obtained represents the
elements in the single column. The Fig 5 represents the
representation of image in 1D form.
4. Artificial Neural Network (ANN):-We have used
Artificial Neural Network (ANN)for training and face
detection .The neural network learns the face patterns
from the training data set and applies it for detecting
face object from the query image. We have used
multilayer perceptron network for training and face
detection .The network consist of input layer, hidden
layer and output layer with tensing transfer function
.The neural network learns the face patterns from the
training data set and applies it for detecting face object
from the query image.
Fig 5 represents the representation of image in 1D form.
3.The same procedure of the conversion of 2D matrix
into 1D matrix is done for all the 50 images. Finally we
get the set of matrix with dimension 486*25 as shown in
the fig 5.We have taken 25 images as training set and
the remaining 25 images as testing set. The total
Fig 7. represents the GUI .
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
TRAINING PHASE
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Fig 8.Represents Training phase
TESTING IMAGES
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Fig 9. The face is detected with maximum accuracy.
IV. CONCLUSION
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