fuzzy LBP for face recognition ppt

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By ABDULLAH GUBBI MADASU HANMANDLU MOHAMMAD FAZLE AZEEM PA College Of Senior IEEE Member, Senior IEEE Member, Engineering EE Dept., IIT Delhi, EE Dept.AMU Karnataka New Dehi, India UP A NOVEL LBP FUZZY FEATURE EXTRACTION METHOD FOR FACE RECOGNITION 1

description

face features are extracted from fuzzy and LBP method tested on SVM and KNN

Transcript of fuzzy LBP for face recognition ppt

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ByABDULLAH GUBBI MADASU HANMANDLU MOHAMMAD FAZLE

AZEEM PA College Of Senior IEEE Member, Senior IEEE

Member, Engineering EE Dept., IIT Delhi, EE Dept.AMUKarnataka New Dehi, India UP

A NOVEL LBP FUZZY FEATURE EXTRACTION METHOD FOR FACE RECOGNITION

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Agenda

Face recognition. Fuzzy logic. Local Binary Pattern. Information set. K-Nearest Neighbour classifier; Support Vector

Machine. Results and Conclusion.

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3 Face recognition Face recognition has received much attention during the past few decades.

Challenges (I) pose variation (front, non-front), (ii) occlusion, (iii) image orientation, (iv) illumination condition and (v) facial expression.

Algorithms (I)Structure-based schemes that make use of shape and other texture of the face along with 3D depth information.(II) Appearance-based schemes that make use of the holistic texture features.

The Eigen faces (PCA) and Fisher faces (LDA) methods are based on the holistic approach .

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4 PCA & LDA The Eigen faces (PCA) and Fisher faces (LDA) methods are based

on the holistic approach for face recognition.

Eigen-faces approach is likely to find the wrong components on images when there is a large variation in illumination, since the data points with the maximum variance over all classes are not necessarily useful for classification.

The Fisher-faces are computationally expensive.

Looking at the shortcoming of the above techniques and taking advantage of Information sets, which have been developed by Hanmandlu to enlarge the scope of fuzzy sets.

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5 Experiment using Haar 10 wavelet de-noising[4]

Recognition rate

PCA LDA KPCA FA

ORL database (with noise)

66.07% 86.07% 49.29% 85.70%

Proposed database (without noise)

66.43% 89.29% 51.07% 86.07%

Where, PCA- Principle component analysis,LDA-Linear Discriminant Analysis,

KPCA-Kernel Principle component analysis and

FA- Fisher Analysis.

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6 Local Binary Patterns

The Local Binary Pattern (LBP) method is widely used in 2D texture analysis. The LBP operator is a non-parametric 3x3 kernel which describes the local spatial structure of an image.

Introduced by Ojala et al.

LBP is defined as an ordered set of binary comparisons of pixel intensities between the centre pixel and its eight surrounding pixels. The decimal values of the resulting 8-bit word (LBP code) leads to 28 possible combinations, which are called Local Binary Patterns

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7 Fuzzy Logic Fuzzy Logic (FL) theory is the extension of conventional (crisp) set theory.

It was introduced by Zadeh .

Deals with the fuzzy sets having imprecise and uncertain data. It handles the concept of partial truth (truth values between completely true and completely false.

Used to model the vagueness and ambiguity in complex systems for which there is no mathematical model to describe.

The drawback of fuzzy sets is that they treat the property or attribute values which we say information source values and their membership function values separately for all the problems dealing with the fuzzy logic theory.

consider a set of students graded based on the performance of the class topper as the benchmark and the student’s individual performance (information source value) is determined by comparing his performance with that of the topper (µ).

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Proposed method Instead of considering the whole image and their gray values, divide

the image into sub images of size 3x3 (enclosed in the window)non overlapping .In case of LBP it is overlapping.

Extract local information using LBP method

We compute a membership function for each window based on the centre pixel of the window using:

Where represent the centre pixel in the window and represents the sum of the gray values in the same window.

The information at the central pixel is given by the product of the membership value and central pixel value as per the concept of information value.

8

),( yxi

icwindow

ci ),( yxi

cwindowc iH

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9 Proposed method conti…. This information is modified by a scale factor = with the result

the scaled information is

where λ is a scaling parameter which is greater than one (λ>1). and

In order to take account of the information from the neighbourhood pixels (information sources), we compute the LBP value for the window.

By taking a clue from the communication theory, the information about the neighbourhood pixels is taken to be the log of the decimal value:

maxF

cS HFH max

50.0max F

),( ccw yxLBPL

wN LH log

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Having the information at the central pixel and the neighbourhood pixels, the total information is taken as the product of these two types of information, given by

The contribution of this method is that it eliminates the shortcoming of LBP approach that ignores the central pixel value by accounting for the information of both the central and the neighbourhood pixels

NSHHH

Proposed method conti….

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11Support Vector Machine (SVM)

A classifier derived from statistical learning theory by Vapnik, et al. in 1992

SVM became famous when, using images as input, it gave accuracy comparable to neural-network with hand-designed features in a handwriting recognition task

Currently, SVM is widely used in object detection & recognition, content-based image retrieval, text recognition, biometrics, speech recognition, etc.

Also used for regression.

V. Vapnik

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12 KNN Classifier

The k-Nearest Neighbour classifier is amongst the simplest of all the machine learning methods.

It is a non-parametric method for classifying objects. Non-parametric in the sense that one need not worry about the underlying structure.

Classification is done based on how much close the test feature vector is to the training feature vectors in the feature space.

An object is classified based on the majority votes of its neighbours. If k = 1, then the object is simply assigned to the class of its nearest neighbour.

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13 some of the subjects in databases

Fig 2. Near-infrared face images of some of the subjects in the CSIST database.

Fig 3. Gray scale face images of some of the subjects in ORL database.

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Experimental Results

ORL Database Poly1 Poly 2 KNN

Training images 7Testing images 3 96.66% 96.66% 92.5%

Training images 3Testing images 7 82.55% 80.35% 82.55%

Training images 5Testing images 5 87.5% 87.5% 88%

Training images 4Testing images 6 88.33% 87.916% 87.08%

The Recognition rates obtained on ORL database with SVM and KNN classifiers

CSIST Near Infra Red Database Poly1 Poly 2 KNN

Training images 1Testing images 3 89.33% 87.66% 89.33%

Training images 2Testing images 2 92% 92% 91.55%

Recognition rate for CSIST near infra red database with SVM and KNN classifier.

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15 Flow chart of implementation

Stop

Input image train or test

tr Normalize image

For number of images

Divide into 3x3 windows

For each window

Compute µ, Compute sumPick centre pixel and

Compute information set or feature

Store features in database

Start

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16Conclusion

A novel approach is presented to account for the information from both the central pixel and the neighbourhood pixels of a face image while matching test sample with the training samples in the face recognition process .

The information value is defined as the product of information source value and its membership function value.

A comparison of performance of the proposed approach is made with that of PCA using two classifiers KNN and SVM. Better results are reported with SVM.

The proposed approach is found to be effective on images having variation in expression, illumination and pose. Further work needs to be done by changing the membership function values. This is possible one way by employing type-2 membership functions in which one of the parameters is changed.

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17References

[1]Laurenz Wiskott, Jean-Marc Fellous, Norbert Kr¨uger, and Christoph von der Malsburg. Face recognition by elastic bunch graph matching. IEEE Trans. On Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997.

[2]M. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces”, IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii, 1991.

[3]P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”,IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, No. 7, 1997, pp. 711-720.

[4]Isra’a Abdul-Ameer Wavelet Based Image De-noising to Enhance the Face Recognition Rate, IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 1, No 3, January 2013.

[5]Khan, M.M. Face Recognition using Sub-Holistic PCA Information and Communication Technologies, 2005. ICICT 2005. First International Conference on pages152 – 157( 27-28 Aug. 2005)

[6]M. Hanmandlu, Information sets and Information Processing, A Research Report, IIT Delhi, March 2011.

[7]T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29, 1996.

[8]Mamta and M., Hanmandlu, M., Robust Ear Based Authentication Using Local Principal Independent Components, Expert Systems with Applications, vol. 40, pp. 6478-6490, 2013.

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THANK YOU