A comparative study of various face recognition algorithms (feature based, eigen based, line based,...

2003 IEEE International Workshop on Computer Architectures for Machine Perception (CAMP)

A COMPARATIVE STUDY OF VARIOUS FACERECOGNITION ALGORITHMS

(Feature Based, Eigen Based, Line Based, Neural Network Approaches)

S.K.SinghI, Mayank Vatsa2, Richa Singh3, K.K. Shukla41,2,3 - Department of Computer Science & Engineering

Institute of Engineering & TechnologyPurvanchal University, Jaunpur (U.P.) - India

4- Department of Computer Sc. & EngineeringInstitute of Technology - BHU (IT-BHU)

Varanasi (U.P.) - India

E-mail: [email protected],3 -mayank [email protected] shuklaLaieee.org

Abstract

As continual research is being conducted in the area of computer vision, one of the most practicalapplications under vigorous development is in the construction of a robust real-time face recognitionsystem. While the problem of recognizing faces under gross variations remains largely unsolved, a

demonstration system as proof of concept that such systems are now becoming practical have been

developed. A system capable of reliable recognition, with reduced constraints in regards to the position

and orientation of the face and the illumination and background of the image has been implemented. In

this paper an attempt is made to compare existing face recognition algorithms which are widely used and

subject of interest. Feature based

Keywords: Face Recognition, Feature Based Approach, Eigen Face, Line Based Approach, Principal

Component Analysis.

Introduction

Automatic human face recognition, a technique that can locate and identify human faces

automatically in an image and determine "who is who" from a database, is gaining more and more

attention in the area of computer vision, image processing and pattern recognition over the last two

decades. There are several important steps involved in this problem: detection, representation and

identification. Based on different representations, various approaches can be grouped into feature-based

and image-based. In order to make this method more feasible, face images should be registered first. In

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addition, conducting correlation in the whole image scale is easy if interfered by the background and is

too time-consuming. Therefore using smaller special region of interests (ROI) that is referred to as

templates is a better alternative. More complicated methods include the elastic template technique,principal component analysis using eigenfaces and the neural network approach. But most of these

recognition systems have a similar disadvantage that they cannot work effectively under varying pose.The strategy adopted in our system is also based on gray level images. To overcome all the drawbacks

mentioned above we also incorporate landmark-based affine registration algorithm to normalize imagescales and orientations. To make the system work under varying head poses, we create a database withmulti-view faces. Therefore there are four major components in our face recognition system: modeling,feature detection, normalization and identification.

In this paper we have presented a comparison between the four face recognition algorithms,feature-based approach for face recognition [6,3], line-based approach for face recognition [10], eigenfaceapproach for face recognition [I], and neural network approach [2,5,7]. As there are so many algorithmsto implement recognition so this comparative study was done in order to compare the efficiency of the

various algorithms in different conditions like for different illumination, zoom; blur ness, pose and headorientation and various other conditions. For this a database of images was made which consisted of the

snaps of various persons taken in various conditions.

In today's world where security is a major topic of concern for most of the people, facerecognition can be used a very useful technique to identify and catch the unwanted persons. Today atmany places in the world, automated systems are there to perform various tasks as security check andvarious others, face recognition technology can be used as a tool for keeping a check on and getting hold

of any desired person.

Face Recognition Algorithms

Feature-based approaches, especially those relying on geometrical features, represent the face as a

multi-dimensional feature vector and determine the identification by the Euclidean distance betweendifferent feature vectors. Image-based approaches rely on the entire image instead of some features. In itssimplest form, the face is represented as a 2D array of intensity values. The feature-based approach hasthe advantage over the image-based approach that it requires less data input but suffers from the

incompleteness of features and difficulty of automatic feature detection. By carefully choosing the regionof interest (ROI) and possibly appropriate transformations, the image-based approaches can give morereliable results than the feature-based approach. In the simplest version of image-based approaches, facesare represented as a 2D array of intensity values and recognition is normally based on direct correlationcomparisons between the input face and all other faces in the database. The image with the highest

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correlation is chosen as the best match (a perfect match gives a value of 1.0). This approach obtains

satisfying results only when conditions are ideal (with equal illumination, scale and pose, etc.). However

ideal conditions are not always present in most cases.

The feature-based technique extracts and normalizes a vector of geometric descriptors of

biometric facial components such as the eyebrow thickness, nose anchor points, chin shape, zeugmaticbreadth, etc. The vector is then compared with, or matched against, the stored model face vectors. This

approach, however, requires the solution of the correspondence problem, that is, the facial vector

components must refer to the facial features in the image. Also, the model face generation is quite time

consuming, particularly for large face databases, and the complexity of geometrical descriptors can be

restrictive. Model generation and matching with non-frontal-parallel poses and varying illumination are

more complex and can only be achieved at the expense of increased computation times.

The line-based is a type of image-based approaches. It does not use any detailed biometric

knowledge of the human face. These techniques use either the pixel-based bi-dimensional array

representation of the entire face image or a set of transformed images or template sub-images of facial

features as the image representation. An image-based metric such as correlation is then used to match the

resulting image with the set of model images. In the context of image-based techniques, two approaches

are there namely template-based and neural networks. In the template-based approach, the face is

represented as a set of templates of the major facial features, which are then matched with the prototypicalmodel face templates. Neural network-based images techniques use an input image representation that is

the gray-level pixel-based image or transformed image which is used as an input to one of a variety of

neural network architectures, including multilayer, radial basis functions and auto-associative networks.

In the line-based algorithm, which is computationally efficient under varying pose handles face

rotations both in and out of the imaging plane, and is robust to variations in scale. The image

representation scheme used is a set of random one-dimensional rectilinear line segments of the gray-level

face image and the matching scheme is an efficient nearest-neighbor classifier. The motivation for our

line-based face recognition scheme is that, even though each line segment only predicts a correct face

marginally better than random, the combination of line segments from a unique face image leads to a high

probability of correct face classification. The idea that 3D object recognition can effectively be

undertaken using multiple redundant 2D views of an object is extended to the idea that a combination of a

set of randomly placed 1D line segments in a 2D object view exploits the coherence in that view.

Therefore, the simple rectilinear line segment primitive forms the basis for our image representation.

In the eigen-face approach, we consider that what aspects of the stimulus are important for

identification, i.e., we want to extract the relevant information in a face image, encode it as efficiently as

possible, and compare one face encoding with a database of models encoded similarly. This is to find the

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principal components of the distribution of faces, or the eigen vectors of the covariance matrix of the set

of face images. These eigen vectors can be thought of as a set of features that together characterize the

variation between face images. This eigen vector can be represented in terms of a face called eigen-face.The faces can also be approximated using only the "best" eigenfaces - those that account for the most

variance within the set of face images. The idea of using eigenfaces was developed by Sirovich and

Kirby's technique that was developed for efficiently representing pictures of face images using principalcomponent analysis. Thus if a multitude of face images can be reconstructed by weighted sums of a small

collection of characteristic images, then an efficient way to learn and recognize faces might be to build

the characteristic features from known faces by comparing the feature weights needed to (approximately)reconstruct them with the known individuals.

In neural network approach we do the preprocessing steps to reduce the dimensionality problem.We use spatial normalization of the face image. We detect the eyes and mouth in face images using the

Generalized Symmetric Transform (GST) and then we wrap the face image to the standard location usingthese points. The main idea behind it is the following: starting with an edge map, every pixel is assigned a

magnitude M that estimates the probability that there is a symmetric spatial configuration of edges around

it, and an orientation that points in the direction of the main axis of symmetry around pixel. The pixel in

the center of a circular, elliptical or rectangular area surrounded by edges, will be assigned a high value ofM. this results in a Symmetry Map, where every pixel has a value, and the highest peaks of symmetrycould be detected. The GST is context free, in the sense that it operates directly on pixels and not onknown pixels. However, it is possible to incorporate application specific information to enhance itsperformance. We have used the following operations on face image in order to detect the location of theeyes and mouth:

* Computation of the symmetry magnitude and orientation.

* Computation of radical symmetry

* Detection of the highest peaks of the regular and radical symmetry in the image.

Detection of the midline of the face image by finding the peak in the autocorrelation functionof the edge image.

* Detection of the eyes and mouth by including geometric considerations.

Comparative Results of Various AlgorithmsLine Based Face Recognition Technology

To investigate the effect of various parameter settings on classification and correctness, we run fourexperiments, varying one parameter at a time. In each experiment, the parameters were resampled overthe combined face database. For example, for evaluating the probability of correct classification (PCC)

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for different numbers of test lines, each set of test lines was obtained by resampling the combined face

database.

In the first experiment, the number of test lines N was varied from 20 to 400. The number of training

lines, Nk, was set to I = 32, and the minimum confidence factor is set to 200, the line dimensionality was

set to I = 32, and the minimum confidence factor was set to zero. This corresponds to a ratio in the

number of training pixels to image size equal to 0.078, equivalent to an effective dimensionality reduction

for model storage by a factor equal to 12.8. As expected, both the computation time and the classification

accuracy increase almost monotonically with the number of test lines. The increase in time is

approximately linear, whiles the accuracy first increases rapidly and then levels out.

In the second experiment, the number of training lines was varied from 50 to 400. The number of testing

lines was fixed at 150, the line dimensionality was set to 32, and the minimum confidence parameter was

set to 0.

Both the time and the classification accuracy increase with increased number of training lines. As

increasing the number of test lines only decreases the variance in the procedure that forms a decision from

the classifications of all test lines.

In the third experiment, the minimum confidence measure factor value was varied from 0.0 to 0.4. The

number of training lines was set to 300, the initial number of testing lines set to 50, and the line

dimensionality set to 32. The experiment was repeated with the number of initial testing lines set to 100.

An improved accuracy for the case when the initial number of test lines is doubled is observed. For

example, for a time equal to 0.7 sec, both cases achieve near 100% accuracy for the people and greater

than 90% accuracy for the views. Thus, we can conclude that we can decrease the number of initial

testing lines without ill-effect so long we increase the minimum confidence measure accordingly and vice

versa.

In the last experiment, the line dimensionality I was varied from 8 to 64. The number of training lines was

set to 300, the initial number of testing lines to 100, and the minimum confidence value was set to 0.1.

The classification accuracy first increases rapidly, then stabilizes with the change occurring between 1= 16

and 24. The timings first decrease, then increase. This is because the confidence value is not set to zero

and small dimensionalities triggered frequent increases in the number of test lines. However, it is perhaps

surprising that, for 1= 16, the classification rate for persons is already 100%.

The results indicate that despite very low PCC of individual lines combining a large number of such

classifications, results in an overall PCC, which is much higher. A number of training and test lines per

face view were chosen to be equal to 280 respectively. A dimensionality of 32 and minimum confidence

measure factor to 0.4 were selected.

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Input Image Database Image (matched)

\/

x

Table 1: results of Line based FRTEigen Faces

To demonstrate the capability and the accuracy of the recognition stage, a selected database of ten facesand several recognition results are presented. The database presented in Figure is a small database that

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Criterion Experimental ResultsProcedure Semi-Automatic

Same image with different name 90% accuracySame image with different brightness Independent of brightness changesSame image with different contrast Independent of illumination changes

* With same posture & head orientation -80%Different image of same person * With different image

Up to 50 - 65%Upto 10°-50%Up to 200 - Fails

Zoom and Blur ness * Fails on zoom (15%)* With little blur ness (70%)

Angle Orientation Can manage the rotation upto 16Independent of brightness, contrast and blur ness

+ ve points * Little differences on images of same person can bemanaged

- ve points * Semi automatic, results dependent upon user'saccuracy

* Fails on zoom and rotation

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was used during the development of the design to test the recognition ability of the system. The faces

presented are the inputs into the training stage where a representative set of eigenfaces were determined.After training, new images are face detected, normalized and entered into the recognition stage foridentification. Comparing the weights of the test face with the known weights of the database performsidentification. Mathematically, a score is found by calculating the norm of the differences between the

test and known set of weights, such that a minimum difference between any pair would symbolize the

closest match.

Input Training Images

The eigenfaces were calculated from the above training images. When the eigenfaces are displayed, theylook like a ghostly face. The eigenfaces are displayed below. For this project 10 eigenfaces were chosen.

This was based on the eigenvectors with the highest eigenvalues.

00 I^ f$ frEigen Faces corresponding to the training images

Recognition is done by finding the training face that minimized the "face" distance with respect to the

input face, i.e., which of the training faces was more alike the input face. This was based on determiningthe Eucludian distance.

Recognition was performed on the 16 faces of the training subjects. A 100% recognition rate was

achieved over the training set. Several pictures not relating to the training images were inputted into the

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program to determine if the algorithm would recognize them. If an unknown image is inputted the

algorithm will return a message stating it does recognize the image.

The program will prompt the user for an image to be detected among the database. When an image isentered into the database, the program is able to process and detect what face image closely resembles the

input image. The algorithm will return the image that closely matches the index of the input image.Input images Result

*X~W_

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2003 IEEE Intemational Workshop on Computer Architectures for Machine Perception (CAMP)

* Speed depends upon the size of image* Can handle little bit of blur ness

- ve points * Can't work on different rotations, angles andorientations

Table 2: results of Eigen face approach

Feature Based

The results are shown below, and are arranged in the manner of successful search, the partial successfulmatch (ifany) and the unsuccessful match, with a note on the specific result for the we employed during

the course ofour experiment.

In this test, the feature vectors of database images are compared with those of test image, and imagehaving maximum number of similar (exact) features is selected. The three best selections are displayed.

U -Successful Matches (image 1, 2, 3)

Unsuccessful Match (image 4)Test Image not in database

The first three images are the example of successful result when the search in database is successful,while in the last case the algorithm shows that the test image is not available in the database. Image no 4

shows that in case the test image is not present in the database, the algorithm gives the three nearest

matches found.

Criterion Experimental ResultsProcedure Semi automatic

Same image with different name 100% accuracySame image with different brightness Independent ofbrightness - 100%Same image with different contrast Independent of contrast - 100%Different image of same person * With same posture & head orientation -85%

* It can't work if features are not availableZoom and Blur ness * Handles the zoom factor - 90%

* Blur ness - 95%Angle Orientation Can manage the rotation up to 10%

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High accuracy rate on slightly different images+ ve points * Very fast after user inputs

* Gives the best three matched imagesSemi automatic, i.e., it needs lots of user interaction

- ve points which causes the effect on results. The result is heavilydependent upon the users accuracy.

Table 3: Results of Feature based approach

Neural Network ApproachWe have performed many experiments for getting the optimum result from this approach. For that sakethe experiment portions namely, effect of background on nearest neighbor classifiers, nearest neighborperformance on warped images, the effect of classification method on the removed-background imageswith warping and gradient normalization, principle component extraction, and eigenvectors, are done.These experimental results are given as follows:

Preprocessing SuccessType ratio

Full image 97.5)0%No 71.88%

BackgroundNo Face 96.25%

Table 4: Effect ofthe background on nearest neighbor classification. Success ratio is given using thenumber of images correctly classified.

The image warp, i.e., the affine transformation that takes the eyes and the tip of mouth to a fixedlocation, has now a strong effect on recognition performance. The effect of image warped transformationand gradient normalization on nearest neiwhbor classification results is exhibited in followinG tables

Tabl

Warp - Gradient Success ratioNo No 71.88%No Yes 73.75%Yes No 81.88%Yes Yes 91.88% |

- 5: Nearest neighbor performance on the warped fFaces.

Classifier Success ratioI NN 91.88%3 NN 51.88%RBF 91.88%

Table 6: The effect of classification method on the removed-background images with warping andgradient normalization.

No. Of Eigen-vectors Success ratio1 18.13%5 71.88%20 88.75%40 93.75%50 95.00%62 94.38%

-1-~~~~~~~~~~~~~~-1 auvi /. un nearesL nignUor ciassiiicaLion using varymg numDer OI pnncipie components extracteuform the images for each person, without background, with warping and gradient.

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- r- - - - - --- --- -r- - -

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2003 IEEE Intemational Workshop on Computer Architectures for Machine Perception (CAMP)

Criterion Experimental ResultsProcedure Automatic

Same image with different name 100% accurcySame image with different brightness * If slight change - 95%

. If large difference then - 8%Same image with different contrast * If slight change - 80%

. If large difference then - 60%Different image of same person . With same posture and head orientation

* With large differences - failsZoom and Blur ness * Fails on zoom

_______________________ ___ * With little blur ness (70%)Angle Orientation Can manage the rotation up to 2%

* Very fast+ ve points * Speed depends upon the size of image

* Can handle little bit of blur ness- ve points * Can't work on different rotations, angles and

onentationsTable 8: Results ofNeural Network Approach

ConclusionIn this paper a comparative study of various face recognition algorithms have been shown. All

these algorithms have some advantages and some disadvantages as well. The line-based algorithm is quitegood due to the fact that a combination of ID line segments effectively exploits the inherent coherence in

a 2D face image view. The main problem with the algorithm lies in the assumption that the face-detectionhas been undertaken prior to the application of the line-based algorithm and that the face boundaries are

available. If the boundaries are largely occluded or indistinguishable from the background, then tie

performance of the algorithm is reduced. The technique of eigenface requires such strict alignment offaces that any inaccuracies in aligning the face will degrade the recognition and generalization ability of

the database.As seen above, all the algorithms have some positive and negative features. Now, we are trying to

make an intelligent system for recognizing faces, which works on the algonithm developed by using thefeatures of all the algorithms so that a robust real time face recognition system can be developed and usedfor various security measures.

Acknowledgement

We are thankful to Dr. K.S. Venkatesh, IIT Kanpur, Prof Y.V.Venkatesh, IISc. Banglore, R.B. Lokesh,IISc. Banglore, and Prof A. Rosenfeld, University of Maryland for their kind support and guidance.

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Reference

[I] M. Turk and A. Pentland," Eigenfaces for recognition", J. Cognitive Neuroscience, vol 3. 1991

[2] S. Edelman, D. Reisfield and Y. Yeshuran, 'Leaming to Recognize faces from Examples".Proc.European Conf Computer Vision, ECCV 92.

[3] R. Brunelli and T. Poggio, Face Recognition: Feature vs Templates", IEEE Trans. Pattern Analysisand Machine Vision.Oct. 1993

[4] A. Pentland, B. Moghaddam and T. Stamner, "' View based and Modular Eigenspace for faceRecognition"', Proc. IEEE Conf. Computer Vision and Pattern Recognition. 1994

[5] M. Kirby and L. Sirovich, "Application of the Karhunen -Loeve Procedure for the Characterisation of

Human Faces," 1990.

[6] 1. J. Cox, J. Ghosn, P. N. Yianilos, "'Feature Based Approach for Face Recognition".

[71 Proceedings ofthe International Conferences on Automatic Face and Gesture Recognition, 1995-1998[8] Proceedings of the International Conferences on Audio- and Video-Based Person Authentication,1997,1999.

[9] M. Turk and A. Pentland, "'Face Recognition Using Eigenfaces" Vision and Modeling Group, TheMedia Laboratory, Massachusetts Institute of Technology.

[10] O.D.Bel and S. Aeberhard, " Line-Based Face Recognition under Varying Pose, IEEE Trans onPattern Analysis and Machine Intelligence, Vol.21, No.10, Oct. 1999.[ 1] R. Brunelli, "'Estimation of Pose and Illumination Direction for Face Processing", Image and Vision

Computing, vol. 15, pp. 74 1-748, 1997.

[12] R. Chellappa, C. Wilson and S. Sirohey, "Human and Machine Recognition of Faces: A Survey",Proc. IEEE, vol. 83, pp. 705-740, 1995.[13] S. Lawrence, C. Giles, A.Tosi and A. Back, "Face Recognition: A Convolutional Neural Network

Approach,"' IEEE Trans. Neural Networks, vol. 8, pp. 98-113, 1997.

[14] S.Lin, S.Kung, and L.Lin, "Face Recognition/Detection by Probabilistic Decision -Based NeuralNetwork,"' IEEE Trans. Neural Networks, vol. 8, pp. 114-132, 1997.

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