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IS1 2008 INFARECIS1 2008 INFAREC 11

INFARECINFARECIntelligent Face Recognition Intelligent Face Recognition

22 IS1 2008 INFARECIS1 2008 INFAREC

INFAREC INFAREC

M.S. Computing (I.T) Spring 2008M.S. Computing (I.T) Spring 2008Project AdvisorProject AdvisorDr. Najmi HaiderDr. Najmi Haider

Lecturer, SZABISTLecturer, SZABISTSubmitted by Submitted by

Shamama Tul Umber ParwaizShamama Tul Umber Parwaiz07721220772122


ABSTRACTABSTRACT

The paper principally deals with the comparison of The paper principally deals with the comparison of different methods for face recognition. This research is different methods for face recognition. This research is based on different sections that describe different based on different sections that describe different techniques. techniques.

For each of the techniques, a short description of how it For each of the techniques, a short description of how it accomplishes the described task will be given.accomplishes the described task will be given.

This report only show a comparison of already made This report only show a comparison of already made researchresearch


Introduction To INFARECIntroduction To INFAREC


INFARECINFAREC

IIntelligent ntelligent FFaceace Rec Recognition.ognition. The face is our primary focus of attention.The face is our primary focus of attention.Role in conveying identity.Role in conveying identity.Face recognition has become an important issue in Face recognition has become an important issue in many applications such as security systems, credit many applications such as security systems, credit card verification and criminal identification.card verification and criminal identification.Comparing different Comparing different FACE Recognition Algorithms.FACE Recognition Algorithms.

66 DEPARTMENT OF COMPUTER SCIEDEPARTMENT OF COMPUTER SCIENCE SZABISTNCE SZABIST

INFARECINFAREC

Goals & Objectives.Goals & Objectives.

ImportanceImportance

Reason For Choosing Reason For Choosing INFARECINFAREC


Why Why FaceFace Recognition? Recognition?

Application of Biometric SystemsApplication of Biometric Systems . .

Highly secured Highly secured

Much Reliable Much Reliable


For forensic and civilian applicationsFor forensic and civilian applications

For business environmentFor business environment

Importance Of Face RecognitionImportance Of Face Recognition


Goals & ObjectivesGoals & Objectives

Implementation of security measuresImplementation of security measures

Accurate recognition. Accurate recognition.

Increase of performance .Increase of performance .


Reason For Choosing INFARECReason For Choosing INFAREC

Flexible Flexible

Self interestSelf interest


Architecture Of INFARECArchitecture Of INFAREC



Input Phase.Input Phase.

Face Detection Phase.Face Detection Phase.

Face RecognitionFace Recognition



Input PhaseInput Phase


Architecture Of INFAREC Contd..Architecture Of INFAREC Contd..

Input Phase Recognition Phase

Knowledge Representation


PatternPattern RecognitionRecognition



Pattern recognition Pattern recognition can be defined as the categorization of input can be defined as the categorization of input data into identifiable classes via the extraction of significant features data into identifiable classes via the extraction of significant features or attributes of the data from a background of irrelevant detail.or attributes of the data from a background of irrelevant detail.

A A pattern class pattern class is a category determined by some given common is a category determined by some given common attributes or features. attributes or features.

A A pattern pattern is the description of any member of a category is the description of any member of a category representing a pattern class.representing a pattern class.

Supervised Supervised pattern recognition is characterized by the fact that the pattern recognition is characterized by the fact that the correct classification of every training pattern is known correct classification of every training pattern is known



Acquisition moduleAcquisition module Pre-processing modulePre-processing module Feature extraction moduleFeature extraction module Classification moduleClassification module Training setTraining set Face library or face databaseFace library or face database


Two Major Approaches in Two Major Approaches in Feature BasedFeature Based Face Recognition Face Recognition

First-order features valuesFirst-order features values

Second-order features valuesSecond-order features values


First-order features valuesFirst-order features values


Second-order features valuesSecond-order features values


EigenfaceEigenface


EigenfaceEigenface

EigenfacesEigenfaces are a set of eigenvectors used are a set of eigenvectors used in the computer vision problem of human in the computer vision problem of human face recognition. The approach of using face recognition. The approach of using eigenfaces for recognition was developed eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face Matthew Turk and Alex Pentland in face classification. It is considered the first classification. It is considered the first successful example of facial recognition successful example of facial recognition technologytechnology


Eigen Face AlgorithmEigen Face Algorithm


Eigen Face AlgorithmEigen Face AlgorithmThe basic idea of the algorithm is develop a system that can The basic idea of the algorithm is develop a system that can compare not images themselves, but these feature weights compare not images themselves, but these feature weights explained before. The algorithm can be reduced to the next simple explained before. The algorithm can be reduced to the next simple steps.steps.

1. Acquire a database of face images, calculate the 1. Acquire a database of face images, calculate the eigenfaces and determine the face space with all them. It eigenfaces and determine the face space with all them. It

will will be necessary for further recognitions.be necessary for further recognitions.

2. When a new image is found, calculate its set of weights.2. When a new image is found, calculate its set of weights.

3. Determine if the image is a face; to do so, we have to see 3. Determine if the image is a face; to do so, we have to see of of it is close enough to the face space.it is close enough to the face space.

4. Finally, it will be determined if the image corresponds to a 4. Finally, it will be determined if the image corresponds to a known face of the database of not.known face of the database of not.


Eigen Face AlgorithmEigen Face Algorithm

M = the highest valueM = the highest value =image difference from average face=image difference from average face


Eigen Face Algorithm contdEigen Face Algorithm contd....

It’s the average faceIt’s the average face

It representing the imageIt representing the image

Covariance Vector Covariance Vector


Eigen Face Algorithm contd..Eigen Face Algorithm contd..

Weight Weight ww is calculated as: is calculated as:

The term weight is used for recognitionThe term weight is used for recognition


Eigen Face Algorithm contd..Eigen Face Algorithm contd..

(a) (b)Figure 2.1 (a) Sample, training set face images. (b) Average face image of the Training set.


Eigenfaces vs Feature Based Eigenfaces vs Feature Based Face RecognitionFace Recognition

Speed and simplicitySpeed and simplicity Learning capabilityLearning capability Face backgroundFace background Scale and orientationScale and orientation Presence of small detailsPresence of small details


Line Edge MapLine Edge Map


Line Edge MapLine Edge Map

LEM uses physiologic features from LEM uses physiologic features from human faces to solve the problem; it human faces to solve the problem; it mainly uses mouth, nose and eyes as mainly uses mouth, nose and eyes as the most characteristic ones.the most characteristic ones.


Line Edge MapLine Edge Map AlgorithmAlgorithm



In order to measure the similarity of human faces the face images In order to measure the similarity of human faces the face images are firstly converted into gray-level pictures.are firstly converted into gray-level pictures.

The images are encoded into binary edge maps using Sobel edge The images are encoded into binary edge maps using Sobel edge detection algorithm.detection algorithm.

The main advantage of line edge maps is the low sensitiveness to The main advantage of line edge maps is the low sensitiveness to illumination changes, because it is an intermediate-level image illumination changes, because it is an intermediate-level image representation derived from low-level edge map representation.[3] representation derived from low-level edge map representation.[3]

The algorithm has another important improvement, it is the low The algorithm has another important improvement, it is the low memory requirements because the kind of data used. memory requirements because the kind of data used.



One of the most important parts of the algorithm One of the most important parts of the algorithm is the Line Segment Hausdorff Distance (LHD) is the Line Segment Hausdorff Distance (LHD) described to accomplish an accurate matching described to accomplish an accurate matching of face images The images are encoded into of face images The images are encoded into binary edge maps using Sobel edge detection binary edge maps using Sobel edge detection algorithm.algorithm.

The main strength of this distance measurement The main strength of this distance measurement is that measuring the parallel distance, we is that measuring the parallel distance, we choose the minimum distance between edges choose the minimum distance between edges


RESULTSRESULTS


RESULTSRESULTS of Eigen Face of Eigen Face

The eigenfaces algorithm is related to the The eigenfaces algorithm is related to the threshold to determine a match in the input threshold to determine a match in the input image It was demonstrated that the accuracy of image It was demonstrated that the accuracy of recognition could achieve perfect recognition; recognition could achieve perfect recognition; however, the quantity of image rejected as however, the quantity of image rejected as unknown increases.unknown increases.

The results show that there is not very much The results show that there is not very much changes with lighting variations; whereas size changes with lighting variations; whereas size changes make accuracy fall very quickly changes make accuracy fall very quickly


RESULTSRESULTS Of LEM Of LEMFor lighting variations the LEM algorithm For lighting variations the LEM algorithm kept high levels of correct recognitions.kept high levels of correct recognitions.

LEM method always managed the highest LEM method always managed the highest accuracy compared with eigenfaces and accuracy compared with eigenfaces and edge map edge map


RESULTS ComparisonRESULTS ComparisonLEM algorithm demonstrated a better accuracy than the LEM algorithm demonstrated a better accuracy than the eigenfaces methods with size variations. While eigenfaces methods with size variations. While eigenfaces difficultly achieved an acceptable accuracy, eigenfaces difficultly achieved an acceptable accuracy, LEM manage to obtain percentages around 90%, LEM manage to obtain percentages around 90%, something very good for a face recognition algorithm. something very good for a face recognition algorithm.

The results from [4] for orientation changes, LEM The results from [4] for orientation changes, LEM algorithm could not beat eigenfaces method. LEM hardly algorithm could not beat eigenfaces method. LEM hardly reach a 70% for all different poses.reach a 70% for all different poses.

LEM is based on face features, while eigenfaces uses LEM is based on face features, while eigenfaces uses correlation and eigenvector to do so.correlation and eigenvector to do so.


CConclusiononclusion


Research ConclusionResearch ConclusionEigenfaces approach excels in its speed and Eigenfaces approach excels in its speed and simplicity and delivers good recognition simplicity and delivers good recognition performances under controlled conditions performances under controlled conditions

eigenfaces approach is very sensitive to face eigenfaces approach is very sensitive to face background and head orientations. Illumination background and head orientations. Illumination and presence of details are reasonably simple and presence of details are reasonably simple problems for the proposed face recognition problems for the proposed face recognition system system


Research ConclusionResearch ConclusionLEM, as a more recent research; allows better LEM, as a more recent research; allows better results for lighting and size variations.results for lighting and size variations.

It beats eigenfaces method with size variation; It beats eigenfaces method with size variation; where it has its most important weakness. where it has its most important weakness.

The basis of the algorithm. LEM is based on The basis of the algorithm. LEM is based on face features, while eigenfaces uses correlation face features, while eigenfaces uses correlation and eigenvector to do so.and eigenvector to do so.


Self ConclusionSelf Conclusion

Both the algorithms has different approach Both the algorithms has different approach to recognize a face the merit of one to recognize a face the merit of one algorithms is the drawback of another one algorithms is the drawback of another one but the combination of these two but the combination of these two algorithms results in providing maximum algorithms results in providing maximum accuracy in face recognition .accuracy in face recognition .


Future Enhancements And Future Enhancements And RecommendationsRecommendations


Future Enhancements And Future Enhancements And RecommendationsRecommendations

After conducting this research work the LineAfter conducting this research work the LineEdge Map is found more reliable now as future Edge Map is found more reliable now as future

directiondirectionResearch of a background removal algorithm.Research of a background removal algorithm.Recognition from multiple views involving Recognition from multiple views involving neural networks.neural networks.Scanner and camera support.Scanner and camera support.Migration to client/server architecture.Migration to client/server architecture.


Other Techniques at a GlanceOther Techniques at a Glance


Other Techniques at a GlanceOther Techniques at a GlanceTECH 1: Humans can recognize familiar faces in TECH 1: Humans can recognize familiar faces in very low-resolution images.very low-resolution images.

TECH 2: The ability to tolerate degradations TECH 2: The ability to tolerate degradations increases with familiarity.increases with familiarity.

TECH 3: High-frequency information by itself is TECH 3: High-frequency information by itself is insufficient for good face recognition insufficient for good face recognition performance. performance. The nature of processing: Piecemeal versus The nature of processing: Piecemeal versus holisticholistic

TECH 4: Facial features are processed TECH 4: Facial features are processed holistically.holistically.

TECH 5: Of the different facial features, TECH 5: Of the different facial features, eyebrows are among the most important for eyebrows are among the most important for recognition.recognition.

TECH 6: The important configures relationships TECH 6: The important configures relationships appear to be independent across the width and appear to be independent across the width and height dimensions.height dimensions.

The nature of cues used: Pigmentation, shape The nature of cues used: Pigmentation, shape and motionand motion

TECH 7: Face-shape appears to be encoded in a TECH 7: Face-shape appears to be encoded in a slightly caricatured manner.slightly caricatured manner.

TECH 8: Prolonged face viewing can lead to high TECH 8: Prolonged face viewing can lead to high level after effects, which suggest prototype-based level after effects, which suggest prototype-based encoding.encoding.

TECH 9: Pigmentation cues are at least as TECH 9: Pigmentation cues are at least as important as shape cues.important as shape cues.

TECH 10: Color cues play a significant role, TECH 10: Color cues play a significant role, especially when shape cues are degraded.especially when shape cues are degraded.

TECH 11: Contrast polarity inversion dramatically TECH 11: Contrast polarity inversion dramatically impairs recognition performance, possibly due to impairs recognition performance, possibly due to compromised ability to use pigmentation cues.compromised ability to use pigmentation cues.

TECH 12: Illumination changes influence TECH 12: Illumination changes influence generalization generalization


Other Techniques at a GlanceOther Techniques at a GlanceTECH 13: View-generalization TECH 13: View-generalization appears to be mediated by appears to be mediated by temporal association.temporal association.TECH 4: Motion of faces TECH 4: Motion of faces appears to facilitate appears to facilitate subsequent recognition.subsequent recognition.Developmental progressionDevelopmental progressionTECH 15: The visual system TECH 15: The visual system starts with a rudimentary starts with a rudimentary preference for face-like preference for face-like patterns.patterns.TECH 16: The visual system TECH 16: The visual system progresses from a piecemeal progresses from a piecemeal to a holistic strategy over the to a holistic strategy over the first several years of life.first several years of life.Neural underpinningsNeural underpinnings

TECH 17: The human visual TECH 17: The human visual system appears to devote system appears to devote specialized neural resources specialized neural resources for face perception.for face perception.

TECH 18: Latency of TECH 18: Latency of responses to faces in infer responses to faces in infer temporal (IT) cortex is about temporal (IT) cortex is about 120 ms, suggesting a largely 120 ms, suggesting a largely feed forward computation.feed forward computation.

TECH 19: Facial identity and TECH 19: Facial identity and expression might be expression might be processed by separate processed by separate systems.systems.


ReferencesReferences


ReferencesReferences[2] Aurélio Campilho, Mohamed Kamel, [2] Aurélio Campilho, Mohamed Kamel, “Image “Image analysis and recognition :international conference, analysis and recognition :international conference, ICIAR 2004, Porto, Portugal, September 29-October 1, ICIAR 2004, Porto, Portugal, September 29-October 1, 2004”2004”. Berlin ; New York : Springer, c2004.. Berlin ; New York : Springer, c2004.

[3] Yongsheng Gao; Leung, M.K.H., [3] Yongsheng Gao; Leung, M.K.H., “Face recognition “Face recognition using line edge map”using line edge map”.Pattern Analysis and Machine .Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: 24 Issue: Intelligence, IEEE Transactions on , Volume: 24 Issue: 6 , June 2002, Page(s): 764 -779.6 , June 2002, Page(s): 764 -779.

[4] M.A. Turk, A.P. Pentland, [4] M.A. Turk, A.P. Pentland, “Face Recognition Using “Face Recognition Using Eigenfaces”Eigenfaces”. Proceedings of the IEEE Conference on . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3-6 June Computer Vision and Pattern Recognition, 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591.1991, Maui, Hawaii, USA, pp. 586-591.

[5] Pentland, A.; Choudhury, T. , [5] Pentland, A.; Choudhury, T. , “Face recognition for “Face recognition for smart environments “smart environments “.Computer, Volume: 33 Issue: 2 , .Computer, Volume: 33 Issue: 2 , Feb. 2000, Page(s): 50 -55.Feb. 2000, Page(s): 50 -55.[6] De Vel, O.; Aeberhard, S., [6] De Vel, O.; Aeberhard, S., “Line-based face “Line-based face recognition under varying pose”recognition under varying pose”.Pattern Analysis and .Pattern Analysis and Machine Intelligence, IEEE Transactions on , Volume: Machine Intelligence, IEEE Transactions on , Volume: 21 Issue: 10 , Oct. 1999, Page(s): 1081 -1088.21 Issue: 10 , Oct. 1999, Page(s): 1081 -1088.

[7] W. Zhao, R. Chellappa, A. Rosenfeld, and J. [7] W. Zhao, R. Chellappa, A. Rosenfeld, and J. Phillips, Phillips, “Face Recognition: A Literature Survey”“Face Recognition: A Literature Survey”. ACM . ACM Computing Surveys, Vol. 35, No. 4, December 2003, Computing Surveys, Vol. 35, No. 4, December 2003, pp.pp. 399–458.399–458.

[8] Face recognition home page: [8] Face recognition home page: http://www.face-rec.orghttp://www.face-rec.org//

[9] Face Recognition by Humans :Nineteen Results [9] Face Recognition by Humans :Nineteen Results All Computer Vision Researchers Should Know All Computer Vision Researchers Should Know About By Pawan Sinha, Benjamin Balas, Yuri About By Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Richard RussellOstrovsky, and Richard Russell

[10] W. Zhao, R. Chellappa, and A. Rosenfeld, [10] W. Zhao, R. Chellappa, and A. Rosenfeld, “Face recognition: a literature survey”. “Face recognition: a literature survey”. ACMACM Computing SurveysComputing Surveys, Vol. 35:pp. 399–458, , Vol. 35:pp. 399–458, December 2003.December 2003.

[11] J.E. Meng, W. Chen and W. Shiqian, [11] J.E. Meng, W. Chen and W. Shiqian, “Highspeed face recognition based on discrete “Highspeed face recognition based on discrete cosine transform and RBF neural networks”cosine transform and RBF neural networks” ; IEEE; IEEE Transactions on Neural Networks, Transactions on Neural Networks, Vol. 16, Issue 3, Vol. 16, Issue 3, Page(s):679 – 691, May 2005.Page(s):679 – 691, May 2005.

[12] Fast Face Recognition Karl B. J. Axnick1 and [12] Fast Face Recognition Karl B. J. Axnick1 and Kim C. Ng11 Intelligent Robotics Research Centre Kim C. Ng11 Intelligent Robotics Research Centre (IRRC), ARC Centre for Perceptive and Intelligent (IRRC), ARC Centre for Perceptive and Intelligent Machines in Complex Environments (PIMCE) Machines in Complex Environments (PIMCE) Monash University, Melbourne, Australia.Monash University, Melbourne, Australia.

http://www.face-rec.org/

http://www.face-rec.org/


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http://attrasoft.com/products/imagefinderdos/#Software

http://attrasoft.com/products/imagefinderdos/#Software


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