HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORK

Novateur Publication’s
International Journal of Innovation in Engineering, Research and Technology [IJIERT]
ICITDCEME’15 Conference Proceedings
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HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND
NEURAL NETWORK
Yogesh B. Sanap
PG Student, Department of Computer Science & Engineering,
TPCT’s College of Engineering,
Osmanabad, Maharashtra, India
Dr. Anilkumar N. Holambe
HOD & P.G. Coordinator, Department of Computer Science & Engineering,
TPCT’s College of Engineering,
Osmanabad, Maharashtra, India
ABSTRACT
Security and authentication of a person is a vital part of any business. There are many techniques used for this
purpose. One of technique is human face recognition. Human Face recognition is an effective means of
authenticating a person. The benefit of this approach is that, it enables us to detect changes in the face pattern of an
individual to substantial extent. The recognition system can tolerate local variations in the face expression of an
individual. Hence Human face recognition can be used as a key factor in crime detection mainly to identify
criminals. There are several approaches to Human face recognition of which Image Processing Principal Component
Analysis (PCA) and Neural Networks have been included in our project. The system consists of a database of a set
of facial patterns for each individual. The characteristic features called ‘eigenfaces’ are extracted from the stored
images using which the system is trained for subsequent recognition of new images.
MANIFESTATION TERM – Biometrics, Neural Networks (NN), Principal Component Analysis (PCA), Eigen
Values, Eigen Vector, Image Processing.
INTRODUCTION
FACE RECOGNITION SYSTEM
A face recognition system is a computer application for automatically identifying or verifying a person from a
digital image or a video frame. One of the ways to do this is by comparing selected facial features from the image
and a facial database. Security and authentication of a person is a crucial part of any industry. There are many
techniques used for security and authentication one of them is face recognition. Face recognition is an effective
means of authenticating a person the advantage of this approach is that, it enables us to detect changes in the face
pattern of an individual to an appreciable extent the recognition system can tolerate local variations in the face
expressions of an individual. Hence facial recognition can be used as a key factor in crime identification and
detection, mainly to identify criminals there are several approaches to facial recognition of which Image processing
principal component analysis (PCA) and neural networks (NN) have been incorporated in our project face
recognition as many applicable areas. Moreover it can be categories into face recognition, face classification, one, or
sex determination. The system consists of a database of a set of facial patterns for each individual. The characteristic
features called ‘Eigen faces’ are extracted from the storage images using which the system is trained for subsequent
recognition of new images.
It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris
recognition systems
BASIC OF FACE RECOGNITION
The first step in Human face recognition system is to detect the Human face in an image. The main objective of
human face detection is to find whether there are any human faces in the image or not. If the Human face is present,
then it returns the location and position of the image and extent of the each Human face. Pre-processing is done to
remove the noise and reliance on the precise registration. The block diagram of a typical face recognition system can
be shown with the help of Figure. The face detection and Human face extraction are carried out simultaneously. The
complete process of face recognition can be shown in the Figure 1.

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Figure 1 Block diagram of a Face Recognition
There are various factors that makes the human face detection is a challenging task. Pose presence or absence of
structural mechanism, Facial look and expression, Occlusion, Image direction. The facial feature detection is the
process to detect the presence and location of features like eyebrow, nose, eyes, lips, nostrils, mouth, ears, etc. this is
done with the assumptions that there is only a single human face in an image. In the Face recognition process the
input image is compared with the stored database. The input image is also called as probe and the database is called
as gallery. Then it gives a matching report and then the classification is done to identify the sub-population to which
new observations belong
LITERATURE REVIEW
TECHNIQUES USED FOR FACE RECOGNITION
a. TRADITIONAL
There some facial recognition algorithms identify faces by extracting important features from an image of
the subject's human face. For example, an algorithm may analyze the relative position, size, shape of the
eyes, eyebrows, nose, cheekbones, and jaw. These features are then used to search for other images with
matching features from stored database. Other algorithms normalize a gallery of face images and then
compress the face data, only saving the data in the image that is useful for human face detection. A probe
image is then compared with the face data. Popular recognition algorithms include Eigen-face, fisherface,
the Hidden Markov model, and the neuronal motivated dynamic link matching.
b. 3D
A newly trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition.
This technique uses 3D sensors to gather information about the shape of a face. This information is then
used to identify unique features on the surface of a face, such as the contour of the eye sockets, nose, and
chin. One advantage of 3D human face recognition is that it is not affected by changes in lighting like other
techniques. It can also identify a human face from a range of viewing angles, including a profile view.
c. SKIN TEXTURE ANALYSIS
It is another trend which uses the visual details of the skin, as captured in standard digital or computer
scanned images. This technique, called skin texture analysis, turns the unique lines, patterns, and spots
apparent in a person’s skin into mathematical calculations. Tests have shown that with the addition of skin
texture analysis, performance in recognizing human faces can increase 20 to 25 percent.
PROBLEM STATEMENT
In face recognition system, the face detection and feature mapping is core concern to analyze the face. There is need of training
for detection of faces from different perspective. The efficiency is the main concern for feature selection and multiple algorithm
proposed for solve the accuracy problems. In the past techniques, there is need to provide the large test or training images to
detect and assign the particular class means efficient image processing. Automatic recognition of human face is a challenging
problem which has received much attention during recent years due to its many applications in different fields. Human Face
recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all
situations.
EXISTING SYSTEM
• Human Face recognition is the computer application technique to recognize a human face. When a person is
registered in a face recognition system, a video camera takes a series of snapshots of the human face and then
represents it by a unique code
• When person has their face verified by the computer system, it captures their current appearance look and compares it
with the facial unique codes already stored in the database system.

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• When human faces match, the person receives authorization for the system; otherwise, the person will not be
authorized. The existing human face recognition system identifies only static face images that almost exactly match
with one of the images stored in the image database.
• When the current image captured almost exactly matches with one of the images which is stored in database then
only the person is authorized and granted access.
• When the current image of a person is considerably different, after comparing with image database say, in terms of
facial expression then person will be denied
PROPOSED SYSTEM
The proposed human face recognition system overcomes certain pitfalls of the existing human face recognition system. It is
based on extracting the important features of a set of human faces stored in the database and performing mathematical
operations for the comparison on the values corresponding to them. Hence when a new image is fed into the system for
recognition the important features are extracted and compute result to find the distance between the input image with the stored
database images. Proposed system can tolerate some variation in the new face image. When the new image of a person varies
from the images of that person stored in the database, the system will be able to recognize the new face and identify person. The
proposed system is better mainly due to the use of facial features rather than the entire face. Advantages in terms of
• Human Face recognition accuracy and better unfair power Computational cost because smaller images require less
processing to train the image processing PCA.
• The use of dominant features and hence can be used as an effective means of authentication
IMPLEMENTATION
FACE RECOGNITION USING NEURAL NETWORK
We classify our project into two parts. These two stages are:-
1) Feature Extraction (Training)
2) Classification (Testing)
The first part of our project is feature extraction (Training). Feature Extraction is done with the help of image
processing PCA (Principle component Analysis) Technique and Classification will done with the help of Neural
Network. Here we considered the Yale Database, in which the database is having the images of 15 persons with 11
different positions. Every image in the database is of size 240 X 300 pixels means each image is having 72000
pixels and the number of images are 165. So the total pixels processing is 72000 x 165. The processing of such a
huge data is difficult and also time consuming. So we required to minimize the data first without interfering the
image features and contents. So to reduce the number of pixels processed per image we use here the concept of
image down sampling.
Training Faces
Step 1: Prepare the data
In this step, the faces constituting the training set (Γ) should be prepared for processing. Firstly we down sampled
these images to 60 X 80 pixels i.e. the down sampling ratio is 0.25. So the number of pixels is reduced from 72000
to 4800 pixels. Due to that, the speed of operation of our project has increased.
Now from those images, we considered either all eleven images for training or depends on our choice, although we
can say that eight images are used for training but it may be possible that subject came in front of system may
having passion from that remaining three images. As the number of images for training is increased the result has
also increased.
Down Sampling = |Xn / N|
Where, Xn- Image Pixel Values and N- Down Sampling Rate. Here we considered N=4. Figure bellow shows the
Down sampling image.
Figure 2 down sampling of Image
Image with pixel
value of 240 X 320
Down Sampling Image with pixel value
of 60 X 80

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The Example of down sampled image is shown in figure bellow. The input image is of size 240 X 300 pixels and the
down sampled image is of size 60 x 80 pixels.
Figure 3 Input images with 240 x 300 pixels Down sampled image with 60 x 80 pixels
• We has arranged all the images in the in the row wise with each row size of 60 X 80 = 4800 pixels per row
Step 2: find the mean
The average matrix Ψ has to be calculated, then subtracted from the original faces (Γ) and the result stored in the
variable ɸi
Ψ = M ∑n-1M Γ
ɸ = Γ – Ψ
Step 3: find the covariance matrix, Covariance C is calculated
C=
• We formed a matrix of size 4800 X 165 pixels. From that we had calculated variance and covariance. After
covariance the new matrix is formed of size 4800 X 4800 pixels
• The formula for variance and covariance is shown bellow
----- Variance
-------- Covariance
Step 4: find the eigenvectors, Eigen-values of the covariance matrix in below step
Description of the correct algorithm for determination of eigenvectors and Eigen-values is absent here, as it belongs
to the standard arsenal of most math programming libraries.
• After that, we had calculated Eigen Values of matrix size 4800 X 4800 and Eigen vector of size 4800 X 1.

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Figure 4 Rough sketches for Eigen value and vector calculation
• From these Eigen values and Eigen vector we drawn the Eigen face. The Eigen faces we calculate are 20
instead of 15 (as we use 15 persons) for our convenience and for result improvement
Step 5: find the principal components
M eigenvectors (Eigen-faces) ui, only M' should be chosen, which have the greatest Eigen-values. The greater the
Eigen-value, the more characteristic features of a human face does the particular eigenvector describe. Eigen-faces
with low Eigen-values can be retrieved, as they explain only a small part of characteristic features of the faces.
After M' Eigen-faces ui are determined, the “training” phase of the algorithm is finished.
• We had calculated the 20 PCA values as we consider here 20 Eigenface. So for each Eigenface the PCA is
being calculated. The size of that calculated PCA is 4800 X 20 for all images. Now, PCA value is 240 X
20 matrixes for every person i.e. for 11 posses of a person. PCA is well known statistical procedure that
can be used to derive important features by reducing the dimensions of given input vector without losing
information. The features of interest may not be available explicitly, and must be derived from the available
attributes of individuals.
The feature extraction step is completed after image processing PCA values are being calculated. Once PCA
values are being calculated, the same PCA values are used for testing purpose to avoid the overhead of the project
and increase the speed of our system.
RESULT
FACE RECOGNITION RESULT
The input image was given to program for recognition. The input images have different positions. For this, whether
the input image with different posses was matched or not was shown in figures bellow.
Figure 5 Wink face position
Training image of size
4800 pixels
Image Matrix of size
4800 X 4800 pixels
After Covariance
calculation
Eigen Vector of size
4800X4800
Eigen Value of
size 4800X1

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Figure 6 Left light position
Figure 7 Surprised Face Position
Figure 8 Wear Glass position
Figure 9 Left light possessions

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CONCLUSION
This work illustrates the use of Neural Network for face detection, which gives the improved result as compared to
conventional face recognition methods. The main limitation of the available (Current) face recognition system is that
they only detect upright faces looking at the camera. Separate versions of the system could be trained for each head
orientation, and the results could be combined using different arbitration methods similar to those presented here. In
previous work, the face position must be center-light position; any lighting effect will affect the system. Similarly
the eyes of person must be open and without glass. If such obstacles are on the face of person, the feature detection
is complicated. Preliminary work in this era indicates that detecting profiles views of human faces is more difficult
than detecting frontal views, because they have smaller amount of stable features and because the input window will
contain more background pixels. When an image sequence is available, temporal coherence can focus attention on
particular portions of the images. As a face moves about, its location in one frame is a strong predictor of its location
in next frame. Standard tracking methods, as well as expectation-based methods, can be applied to focus the
detector’s attention. Other methods of improving system performance include obtaining more positive examples for
training, or applying more sophisticated image preprocessing and Normalization techniques. The previous work
required more complication in systems as well as the result of matching is not up to the satisfaction of any system.
In our system special face features are not required for recognition process. The face features are detected using
PCA. The feature face formed is Eigen face. Due to Eigen face, calculations in the specific face features are not
required. So the overhead of the system is reduced as number of user of the system is going to increase.
The results we got here using Yale face database is up to the satisfaction of requirement of any system. As we were
worked on more posses of the persons, so for our system, the center-light position is not required every time. For our
algorithm, we say that as numbers of images for training are more and the epochs are more the result is increases.
But the problem is that time require to complete the recognition is increases. As numbers of images are increases,
time required to complete the recognition is increases.
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[9] T. Kathirvalava kumar and P. Thangavel, “A Modified Back-Propagation Training Algorithm for Feedforward
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HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORK

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