1. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
53
COMPARISON OF THE PERFORMANCE OF EIGNFACE AND
FISHERFACE ALGORITHM
Girish D. Bonde
Assistant Prof, Department of E&TC
J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA
O.K.Firke
Assistant Prof, Department of E&TC
J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA
G.L.Attarde
Assistant Prof, Department of E&TC
J.T.Mahajan College of Engineering, Faizpur, Maharashtra, INDIA
ABSTRACT
In this paper, we implemented eigenface based face recognition and tried to compare the
results with fisherface algorithm. The process required preprocessing. The images had to be resized
to a consistent size. The database used included cropped faces of various sizes. Hence the need for
face detection was eliminated. We tried to compare two of the most frequently used algorithms;
Eigenface and Fisherface. We compared the performance of each algorithm against two constraints.
Pose and the size of training data. Testing dense sparse database with both algorithms. The
performance of Eigenface is 100% and 70% with respectively dense and sparse database. The
performance of Fisherface is 80% with sparse database. The effectiveness of Fisherface across pose
is good, even with limited data and Eigenface across pose is some with enough data. Our study has
shown us that Fisherface algorithm is robust in both cases. This leads us conclude that the Eigenface
algorithm is beneficial when the database is large. But given the robustness of the Fisherface
algorithm, it would be the algorithm of choice if the resources are not a problem.
Keywords: Eigenface; Fisherface
INTERNATIONAL JOURNAL OF ELECTRONICS AND
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 5, Issue 6, June (2014), pp. 53-60
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2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
54
I. INTRODUCTION
The face plays a major role in our social intercourse in conveying identity and emotion. The
human ability to recognize faces is remarkable. We can recognize thousands of faces learned
throughout our lifetime and recognize the faces at a glance even after few years. The skill is quite
robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and
distractions such as glasses or changes in hairstyle.
We have implemented the eigenface and fisherface algorithms and tested them against two
face databases, observing results across pose (out-of-plane face rotation). We evaluated performance
against databases with both densely-sampled and sparsely-sampled facial poses.We have also
extended the work towards automatic estimation of pose parameters.
Given a training database of pre-processed face images, train an automated system to recognize the
identity of a person from a new image of the person. Examine sensitivity to pose using the eigenface
approach suggested in [1,2] and the fisherface approach developed in [3].
1. Comparing results of eigenface & fisherface across pose.
2. Testing dense and sparse training databases.
II. RELATED WORK
The Eigenface is the first method considered as a successful technique of face recognition
[1,2,11]. The Eigenface method uses Principal Component Analysis (PCA) to linearly project the
image space to a low dimensional feature space.
The Fisherface is an enhancement of the Eigenface method [3,8]. The Eigenface method uses
PCA for dimensionality reduction, thus, yields projection directions that maximize the total scatter
across all classes, i.e., across all image s of all faces. The PCA projections are optimal for
representation in a low dimensional basis, but they may not be optional from a discrimination
standpoint. Instead, the Fisherface method uses Fisher’s Linear Discriminant Analysis (FLDA or
LDA) which maximizes the ratio of between-class scatter to that of within-class scatter.
III. COMPARISION BETWEEN EIGENFACE AND FISHERFACE
Eigenface and Fisherface are global approach for face recognition takes entire image for a 2-
D array of pixels. Both methods are quite similar as Fisherface is a modified version of Eigenface
[4]. Both make use of linear projection of the images into a face space, which take the common
features of face and find a suitable orthonormal basis for the projection. The difference between them
is the method of projection is different; Eigenface uses PCA while Fisherface uses FLD. PCA works
better with dimension reduction and FLD works better for classification of different classes.
A. Eigenface
Eigenface is a practical approach for face recognition. Due to the simplicity of its algorithm,
we could implement an Eigenface recognition system easily. Besides, it is efficient in processing
time and storage. PCA reduces the dimension size of an image greatly in a short period of time. The
accuracy of Eigenface is also satisfactory (over 90 %) with frontal faces[1]. However, as there has a
high correlation between the training data and the recognition data.
B. Fisherface
Fisherface is similar to Eigenface but with improvement in better classification of different
classes image. With FLD, we could classify the training set to deal with different people and
different pose. We could have better accuracy in various pose than Eigenface approach. Besides,

3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
55
Fisherface removes the first three principal components which is responsible for light intensity
changes, it is more invariant to light intensity.
Fisherface is more complex than Eigenface in finding the projection of face space.
Calculation of ratio of between-class scatter to within-class scatter requires a lot of processing time.
Besides, due to the need of better classification, the dimension of projection in face space is not as
compact as Eigenface, results in larger storage of the face and more processing time in recognition.
Facial recognition software was developed using the MATLAB programming language by the
MathWorks. This environment was chosen because it easily supports image processing, image
visualization, and linear algebra. The software was tested against UMIST database. UMIST was
created by Daniel B. Graham, with a purpose of collecting a controlled set of images that vary pose
uniformly from frontal to side view. The UMIST database has 565 total images of 20 people. The
UMIST database images, displayed below, has uniform lighting and pose varying from side to
frontal.
Figure 1: UMIST database Images
C. Comparison by Size of training data
For these results, 20 recognition faces (one for each person) were randomly picked from the
database, leaving 545 photos to use as training faces. Mp, the number of principal components to
use, was chosen as 20.
All 20 of 20 images were correctly recognized, the result is 100%, confirming the very good
performance of eigenface with densely and uniformly sampled inputs. For this same database and
setup, fisherface performs very similarly.

4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
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Figure 2: UMIST Results Using Eigenfaces in Densely Sampled Database
D. Comparison by Image pose
For these results, 20 recognition faces (one for each person) were randomly picked from the
database, then 60 more photos were used as training faces. Three training faces were picked for each
person: a frontal, side, and 45-degree view[4].

5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
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Figure 3: UMIST Results Using Fisherface in Sparsely Sampled Database.

6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
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Out of the 20 faces, 16 were correctly classified in the 1st match. Also notice that this
approach is rather pose invariant. it often (13 times) picks out all 3 training images from the
database[4].
Figure 4 : UMIST Results Using Eigenface in Sparsely Sampled Database.

7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 –
6464(Print), ISSN 0976 – 6472(Online), Volume 5, Issue 6, June (2014), pp. 53-60 © IAEME
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For comparison, the same setup is run using the eigenface algorithm. Here 14 of the 20 faces
are correctly classified, and all 3 correct images are never found. Clearly, the fisherface algorithm
performs better under pose variation when only a few samples across pose are available in the
training set.
TABLE I. COMPARING EIGENFACE AND FISHERFACE
Fisherface Eigenface
Computational Complexity Slightly more complex Simple
Effectiveness Across Pose Good, even with limited data Some, with enough data
Sensitivity to Lighting Little Very
We find that both the Eigenface and Fisherface techniques work very well for a uniformly
and densely sampled data set varied over pose. When a more sparse data set across pose is available,
the fisherface approach performs better than Eigenface[4].
IV. CONCLUSION AND FUTURE WORK
The Eigenface and Fisherface method were investigated and compared. The comparative
experiment showed that the Fisherface method outperformed the Eigenface method. The usefulness
of the Fisherface method under varying pose and varying sizes of training databases was verified.
Also our results show that patch-based representation is suitable for face pose estimation.
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