ENSEMBLE METHODS OF FACE RECOGNITION BASED ON
BIT-PLANE DECOMPOSITION
INTRODUCTION
Face recognition has become one of the latest research subjects of pattern
recognition and image processing. Although many face recognition techniques have been
proposed and many achievements have been obtained, we can’t get high recognition rate due to
the changes of face expression, location, direction and light. In this paper we study human face
recognition based on ensemble techniques. In order to improve diversity of component
classifiers, the idea of bit-plane decomposition is used and moving window classifier is used as a
basic individual classifier. The quantized pattern representations’ layers are used jointly to make
a decision. And we mainly study several fused methods which include product, sum, majority
vote, max, min and median rules. Experiments results with face images databases show that
fusion of multiple classifiers has good classification performance. Moreover, we compare
different multiple classifier schemes with other human face recognition methods.
With the rapid development of computer technology, face recognition has become one of
the latest research subjects of pattern recognition and image processing. Face recognition has two
main tasks: face detection and location, that is, to find the face and the location of it from the
input images and to separate the face from the Background. Face feature extraction and
recognition, that is, to extract face feature and match recognition after the detection and location
of the face image preprocessing. The background of images is often controlled or similar
controllable in current studied face recognition system. Detection and location of the face image
in the complexity background is paid more attention to. Face recognition will bring about the
diversity with the changes of face expression, location, direction and light, and face feature
extraction will face much difficulty. As we all know that face images recognition is thought to be
a challenging problem. In the field of video surveillance, criminal identification, bank card and
credit card user identification, and many others, face images recognition has been used widely.
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And for that, many algorithms have been proposed, such as, researchers have presented a
scheme using features that based on ratios of geometric distances. They also presented a scheme
based on eigen faces, and an HMM based approach which was later extended using a pseudo- 2D
HMM. A scheme combining self-organizing maps with a convolutional neural network has been
proposed for some years. A probabilistic decision based neural network, and a simple n-tuple
based method designated the continuous n-tuple classifier have also been introduced by
researchers.
METHODOLOGY
In this paper we introduce the idea of bit plane decomposition and the individual
classifier is presented and the combination methods are discussed which include the product rule,
the sum rule, the majority vote rule, the maximum rule, the minimum rule and the median rule.
Bit-plane decomposition is presented by Schwarz and its basic idea is to
decompose an image into a collection of binary images. Thus, any grey-scaled image can be split
into a series of binary layers. For bit-plane decomposition, the gray levels of the gray-scaled
image are represented in binary. Therefore, for possible distinct gray levels L, the each pixel of
the image is represented by a k ( klog2 L
the significant loss of information and this often adversely affects the performance of the
classifier. If L is too large, it will be time-consuming. So the suitable size is necessary for value
of L. The image is decomposed into k layers where layer ‘i’ is composed of the i-th bits of the
gray level values. Thus, layer ‘1’ is formed be collecting all the least significant bits (LSB) and
layer ‘8’ is formed by collecting the Most Significant Bits (MSB) of the binary coded gray-scale
image. Fig illustrates bit-plane decomposition and gives the 8 layers of the gray-scaled image.
And it can be seen that the lower layers only include insufficient information, the higher layers
include much significant information.
For the convenience of understanding, we introduce an individual classifier firstly, i.e. the
Moving Window Classifier (MWC) which is an enhanced n-tuple on classification system.
MWC is used as the basic individual classifier in the implementation of our multiple classifier
systems.
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We choose MWC as basic individual classifier owing to its fast and accurate recognition
performance. And then, we give briefly Moving Window Classifier in the following. Fig can
illustrate the MWC scheme. First we define a window which is smaller than the image. Only a
portion of the image can be seen through this window. Then the window is moved left to right
and top to bottom in single pixel displacement steps until the entire image is covered. Features
are extracted from this part image and a sub-classifier assigns scores corresponding to the
likelihood of the pattern viewed belonging to the individual classes. When the window is moved,
part classification is carried out for all different window positions. A decision fusion stage then
combines these partial classification scores and, accordingly, assigns a class label to the test
image as a whole.
The moving window classifier
Researchers have show that ensemble of classifiers is generally better than a single
classifier. In this paper, we study ensemble methods based on bit-plane decomposition for face
recognition. The algorithm mainly consists of two parts. Firstly, data set is divided into training
set and testing set. The training set is used for training the classifier and building the basic
classifiers, the testing set is used for testing. Secondly, the training set is decomposed to produce
the binary layers. MWC is trained using each of the decomposed layers and finally a fusion
mechanism is used to combine the individual classifier’s outputs in the ensemble and generate
the final decision. We can use Fig. illustrate the process which is shown in . In the following, we
describe the algorithm.
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References:
1. M.S. Hoque, M.C. Fairhurst, “Face recognition using the moving window classifier,” in:
Proceedings of 11th British Machine Vision Conference (BMVC2000), vol. 1, Bristol, UK,
(2000), pp. 312–321.
2.
H.M.Dong, J. Gao, “Fusion of multiple classifiers for face recognition and person
authentication,” Journal of System Simulation, Vol. 16 No. 8, 1004-731X (2004) 08-184905,Aug. 2004.
3. X. Wang, X. Tang, “Random sampling lda for face recognition,” in 2004 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR’04), 2, pp. 259-265,
2004.
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
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