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Face Recognition with Rough-Neural Network: A Rule Based Approach BY Dr. M. M. Raghuwanshi NYSS College of Engineering and Research, Nagpur (M.S.), India [email protected] Kavita R Singh Department of Computer and Information Technology Yashwant Rao Chavan College of Engineering, Nagpur (M.S), India [email protected]

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  • Face Recognition with Rough-Neural Network:

    A Rule Based Approach

    BY

    Dr. M. M. RaghuwanshiNYSS College of Engineering and Research, Nagpur (M.S.), India

    [email protected]

    Kavita R SinghDepartment of Computer and Information Technology

    Yashwant Rao Chavan College of Engineering, Nagpur (M.S), [email protected]

    mailto:[email protected]:[email protected]

  • Outline

    Objective.

    Introduction.

    Block diagram for proposed system.

    Approach.

    Experimental Result.

    Future Scope.

    References.

  • Objective

    The objective of the proposed paper is to present a pattern recognition approach to face recognition that incorporates rough set theory, parameterized approximation spaces, and rough neurocomputing.

  • INTRODUCTION

  • General Biometric model

    A biometric system is essentially a pattern recognition system which makes a personal identification by determining the authenticity of a specific physiological or behavioral characteristic possessed by the user.

  • Facial Recognition

    Facial recognition is one of the example of biometrics.

    Face Recognition is oldest, natural method of visual interaction.

    Face as a biometric has the distinct advantage over other modalities such as fingerprint , DNA and iris recognition.

    It can be used for surveillance purposes.

  • Rough Sets

    Offer mathematical tools to discover hidden patterns in data.

    Rough set theory is a generalization of the classical set theory for describing and modeling of vagueness in ill defined environment.

    The focus of rough set theory is on the ambiguity caused by limited discernibility of objects (lower and upper approximation of concept).

    A (rmf) makes it possible to measure the degree that any specified object with given attribute values belongs to a given set X .

    B

    UpperApproximation X

    Set X

    LowerApproxima tion X

    B

  • PROPOSED SYSTEM

    Database offaces

    Faces areread from d/b

    for feature extraction

    Decision table

    (Rough Set Approach)Algorithm to

    compute equi-valence classes

    [f]B Algorithm to Compute rmf value

    Algorithm for Select Rule(that identify condition

    Vector in one of the rulesR that closely matches

    cexp )

    Approximation Neuron Decider Neuron

    R={ci => di}

    Cexp [ rmf1,rmf2,…..rmfn]

    d i

  • System Development

    The Olivetti Oracle 3 Research Lab (ORL) database is used in the experiment.

    The ORL dataset consists of 400 frontal faces.

    The size of each image is 92x112 pixels, with 256 grey levels per pixel.

    Our system does not include a face detection step.

    The input of our system is a complete face in the image that have already been detected.

    Face classification is performed with approximation-decider neural network.

  • Some test faces from ORL database

  • Approximation Neuron

    compute rmf value

    Flow Graph for Basic Approximation Neuron Computation

    construct rough set

    New obj

    B, F[f]B

    Let ,B , F , [ f ]B denotes set of attributes , set of neuron inputs (stimuli), and equivalence class containing measurements derived from known objects, respectively.

    = upper approximation.fp being the next predicted input.

    FB pB

    FBff ,

    FB

  • Decider Neuron

    A decider neuron implements a select rule algorithm.

    Select ruledi

    Flow graph for decider neuron

    R={ci => di}

    ei = Where,

    v1, v2,… vn be condition vector values of new object. vaexp1, ..vaexpn be condition vector values of ai in rule (R={ci => di} ) of set R. The elements of set R are rules which have been derived from a decision table using rough set theory

    Yrule = min( ei, di) =ei, indicates the relative error in a succesful classification.(if d 0)

    pBFB

    ff ,

    k

    i i

    ii

    v

    vv

    1

    exp

  • Feature Extraction

    1) Control points were selected to pin point the eye balls and the corresponding Euclidean distance was measured.

    2) The centre of the line joining the eyeballs was bisected to give an idea about the ridge of the nose. This point was found to be a suitable point to measure the length of the nose in a majority of the cases.

    3) The tip of the nose was pointed by another control point. The distance between this point and the point extracted in step 2 was measured to find the length of the nose to a suitable degree of accuracy.

  • 4) The extreme right point of the base of the nose was pointed to be one control point to give an idea about the width of the nose. This point was found to be a suitable point to measure the width of the nose in a majority of the cases.

    5) The extreme left point of the base of the nose was pointed to be another control point. The distance between this point and the point extracted in step 4 was measured to find the width of the nose to a suitable degree of accuracy.

  • Decision Tableobj distance between two eye

    balls(a1)

    nose length(a2)

    nose width(a3) decision attribute

    1 33.422 22.000 17.029 1

    2 36.281 23.000 20.025 1

    3 33.530 10.029 0.000 0

    4 30.000 19.026 19.026 1

    5 33.530 29.120 18.000 1

    6 25.790 22.000 17.263 1

    7 33.422 21.000 16.031 1

    8 25.790 21.378 15.033 1

    9 34.771 21.024 19.026 1

    10 15.728 0.000 0.000 0

    Table I. Decision Table showing training data

    (The row values are sample values)

  • The few domain of the decision attributes are as follows:

    a1(33.422) AND a2(22.000)ANDa3(17.029) di(1)

    a1(36.281)AND a2(23.000)ANDa3(25.025) di(1)

    a1(33.530)ANDa2(10.090)AND a3(0.000) di(0)

    a1(30.000)AND a2(19.026)ANDa3(19.026) di(1)

    a1(33.530) AND a2(29.120) AND a3(8.000) di(1)

    a1(25.790) AND a2(22.000)AND a3(17.263) di(1)

    The above rules are incorporated in a decider neuron repository (storage of rules associated with a decider neuron).

  • Implementation of Rough Neural Network

    The rough neural network is implemented with two layers to analyze the data obtained for different features of face using Matlab: an input layer consisting of 3 upper approximation neurons, and an output layer with a single decider neuron

    Approximation-Decider neural network architecture

  • Experimental Result

    We first conducted experiments using all faces without spectacles, which consists of four views of 30 persons.

    Among which two are used for training and other are used for testing.

    As the result of the experiment the success rate of the proposed algorithm is very close to the target value. The result of the classification system is shown in graph.

    The training set consists of 30 subjects and test sets contained 20 individuals.

    Comparison between outputs of rough neural network and target values.

  • FUTURE PROSPECTS

    There is a scope that more features can be extracted. We have carried out experiments for classification system only for some limited numbers of images which can be extend for more number of images.

    Further improvements can be done to improve the results so as to get greater accuracy of classification.

    The comparative analysis of this technique with other available techniques PCA, LDA etc can considered as one of the future prospects.

  • REFERENCES

    [[1] www.biometrics.gov

    [2] J. Komorowski; Z. Pawlak; L. Polkowski; A. Skowron: “Rough sets: A tutorial”. In: S. K. Pal; A. Skowron (Eds.), Rough Fuzzy Hybridization: New Trend in Decision-Making Singapore: Springer-Verlag, 1999, 3-98,(1999).

    [3] S.K Pal; J. F. Peters; L. Polkowski; A .Skowron: “Rough Neural Computing. An Introduction”. In Pal et al [16].

    [4] J. F. Peters; A. Skowron; L. Han; S. Ramanna: “Towards Rough Neural Computing Based On Rough Membership Functions: Theory and Application” pp. 611-618, (2001).

    [5] Z. Pawlak; J. Grzymala-Busse; R. Slowinski and W. Ziarko: “Rough Sets” COMMUNICATION OF THE

    ACM,VOL.38,NO.11,NOV(1995)

    [6] J.F.Peters and Marcin S. Szczuka: “Rough Neurocomputing: A Survey of Basic Models of Neurocomputation” J. J. Alpigni et al. (Eds): RSCTC 2002. LNAI 2475, pp. 308 - 315, 2002 © Springer-Verlag Berlin Heidelberg (2002).

    [7] Szlavik, Z., Sziranyi, T., “Face Analysis Using CNN-UM,” Analogic and Neural Computing Laboratory,Hungarian Academy of Sciences, 1-6.

    [8] Woodward, J., D., Horn, Jr., C., Gatune, J., Thomas, A., Biometrics: A Look at Facial Recognition, RAND Publishers, (2003).

    [9] Xu, C., Wang, Y., Tan, T., “Robust Nose Detection In 3D Facial Data Using Local Characteristics,” IEEE, (2004).

    [10] Gurbuzy, S., Kinoshitay, K., Kawatoz, S., “Real-time Human Nose Bridge Tracking in Presence of Geometry and Illumination Changes,” ATR Human Information Science Lab., Kyoto, Japan ATR IRC/MIS Labs., Kyoto, Japan

    [11] Szlavik, Z., Sziranyi, T., “Face Analysis Using CNN-UM,” Analogic and Neural Computing Laboratory, Hungarian Academy of Sciences, 1-6.

    [12] Kawaguchi, T., Hidaka, D., Rizo, M., “Detection of Eyes from Human Faces by Hough Transform and Seperability Filter,” IEEE, (2000), 40-52.

    [13] Karl B. J. Axnick1., Kim C. Ng1., “Fast Face Recognition” Intelligent Robotics Research Centre (IRRC), ARC Centre for Perceptive and Intelligent Machines in Complex Environments (PIMCE) Monash University, Melbourne, Australia.

    http://www.biometrics.gov/

  • [14] W. Zhao, R. Chellappa, and A. Rosenfeld, “Face

    Recognition: a literature survey”. ACM Computing Surveys,

    Vol. 35:pp. 399–458, December 2003.

    [15] Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W., “Rough Sets,” COMMUNICATION OF THE ACM, (1995), VOL.38, NO.11, pp. 89-95.

    [16] en.wikipedia.org/wiki/Rough-set

    [17] Yao, Y.Y., “Semantics of Fuzzy Sets in Rough Set Theory,”

    [18] Pal, S., K., Peters, J., F., Polkowski, Z., Skowron. A., “Rough Neural Computing: An Introduction,” In Pal et al [16].

    [19] Peters, J., F., Skowron, A., Han, L., Ramanna, S., “Towards Rough Neural Computing Based On Rough Membership Functions: Theory and Application,” Springer –Verlag, (2001), pp. 611-618.

    [20] Peters, J., F., Szczuka, S., M., “Rough Neurocomputing: A Survey of Basic Models of Neurocomputation,” Springer-Verlag Berlin Heidelberg, (2002), pp. 308-315.

    [21] ORL web site: http://www.camorl.co.uk.

    http://www.camorl.co.uk/

  • THANK YOU!!

    Face Recognition with Rough-Neural Network:�A Rule Based ApproachOutlineObjective � � � �� INTRODUCTION �General Biometric modelFacial RecognitionRough Sets PROPOSED SYSTEMSystem DevelopmentSlide Number 10Approximation NeuronDecider NeuronFeature ExtractionSlide Number 14Decision Table Slide Number 16Implementation of Rough Neural Network Experimental Result FUTURE PROSPECTS REFERENCESSlide Number 21Slide Number 22