Bometrc Sgnature Processng & Recognton Usng Radal Bass Functon Network Ankt Chadha, Neha Satam, and Vbha Wal Abstract- Automatc recognton of sgnature s a challengng problem whch has receved much attenton durng recent years due to ts many applcatons n dfferent felds. Sgnature has been used for long tme for verfcaton and authentcaton purpose. Earler methods were manual but nowadays they are gettng dgtzed. Ths paper provdes an effcent method to sgnature recognton usng Radal Bass Functon Network. The network s traned wth sample mages n database. Feature extracton s performed before usng them for tranng. For testng purpose, an mage s made to undergo rotaton-translaton-scalng correcton and then gven to network. The network successfully dentfes the orgnal mage and gves correct output for stored database mages also. The method provdes recognton rate of approxmately 80% for 200 samples. Keywords- Database, Feature extracton, Radal Bass Functon Network, Sgnature recognton. I I. INTRODUCTION n everyday lfe there are many places where we are needed to gve dentfcaton to gan access. Its examples nclude nternet account, credt cards, ATM machnes, etc. We have to frst provde our dentfcaton and then we can access them. These dentfcatons are generally a logn ID and an alphanumerc (maybe wth specal characters) password or a Personal Identfcaton Number (PIN). Man drawback of ths system s that, they provde verfcaton and not actually dentfcaton. These two terms, though used nterchangeably, are dfferent. When a system verfes a password, t ust checks whether the password s rght or wrong. It doesn t dentfy whch person s ganng access. Hence, anyone havng knowledge of another person s password can easly get hs/her data and can tamper t. Wth the advent of new technology, these systems are beng replaced by much more advanced technques to dentfy a person. These technques are called bometrcs, whch nvolve checkng a person s bologcal trats such as face, retna, fngerprnt, rs, voce, sgnature etc. Formally, bometrcs refers to the dentfcaton of humans by ther characterstcs or trats. The system already has the samples Manuscrpt receved September 30, 2013. A. R. Chadha s wth the Vdyalankar Insttute of Technology, Mumba, 400037, INDIA (phone: +91-9167884425; emal:ankt.chadha@vt.edu.n). N. S. Satam s wth the Vdyalankar Insttute of Technology, Mumba, 400037, INDIA (phone: +91-9920395727; emal:neha.satam@vt.edu.n). V. S. Wal s an Assstant Professor n the Electroncs and Telecommuncatons Engneerng Department, Vdyalankar Insttute of Technology, Mumba, 400037, INDIA (e-mal: vbha.val@vt.edu.n). of characterstcs of all users. When a person provdes hs/her dentty, say fngerprnt, system checks t wth the records n database. When any of records match wth the one beng provded for authentcaton, the user s dentfed by system & gven access. Some of the applcatons of bometrc recognton nclude drvng lcenses, mmgraton, natonal ID, passport, voter regstraton, securty applcatons, medcal records, personal devce logon, desktop logon, human-robot nteracton, human-computer nteracton, smart cards etc. There are certan bologcal measurements whch qualfy to be a bometrc. As sad above, face, retna, rs, sgnature are used as bometrc characterstcs because they are dfferent for dfferent people. Followng are some requrements [1] whch are needed to be satsfed n order to use them as bometrc characterstcs: 1. Unversalty: everyone should have the characterstc. 2. Unqueness: two people should have some dfference n characterstc. 3. Durablty: the characterstc should reman same over the tme. 4. Collectvely: the characterstc should be avalable n quantty. Though above are the propertes of characterstcs, practcally, we have to consder some other ssues also [1]: 1. Performance: the characterstcs should gve excellent speed and accuracy wth mnmum explotaton of resources. 2. Acceptablty: people should accept the use of characterstc n ther daly lfe. 3. Crcumventon: represents how easly the system can be deceved. A plan bometrc system s equpped wth sensory unt, a unt for feature extracton, a unt to perform the matchng and decson-makng unt. The sensory unt collects the bometrc data of user. For the sgnature recognton purpose, a dgtal tablet along wth pen-lke stylus s provded whch works as sensor. The promnent features of data are extracted usng feature extracton unt. These features sgnfy the orgnal data but n usng lesser attrbutes. They can be poston and orentaton of certan ponts, curved lnes n sgnature, underlned alphabets etc. Then ths feature set s matched aganst every set stored n the database and a matchng score s generated. Ths score
depends on how many ponts are matched between data n database and newly produced data. Once ths score s obtaned then t s responsblty of decson-makng unt whether to accept the ndvdual as dentfed user or not. The age-old method for sgnature verfcaton s manual one, n whch a person manually checks the sgnature. If both the sgnatures are suffcently smlar, then the access s granted. An automated sgnature verfcaton process wll help mprove the scenaro. Hgh-end bometrc methods nclude rs, retna, face and fngerprnt based dentfcaton and verfcaton. Even though human features such as rs, retna and fngerprnts are fxed for a person and the varatons n the respectve bometrc attrbute are low, specal and relatvely expensve hardware s needed for data acquston n such systems. For the varous purposes such as bankng, fnancal transactons, document authentcaton, sgnature s used snce long tme. Also, the verfcaton s relatvely less expensve than other bometrc systems [2]. Though the verfcaton s effcent, t s dffcult. There are some varatons n a person's sgnature over the tme, whch needs to be accounted. Examples of the varous varatons observed n the sgnature of an ndvdual have been llustrated n Fg. 1. Every tme sgnature s made, t dffers from prevous one n some angle or scale or curvng of letters. Fg 1: Varatons n the sgnature of an ndvdual Ths nvolves elmnatng or reducng the rotaton, scalng and translaton factors between the orgnal and the newly produced sgnatures. For the verfcaton & dentfcaton purpose, neural network can be employed. Fg.2 shows block dagram of proposed system, where all the effects of new sgnature are reduced and t s matched aganst those n database usng neural network. New Image Rotaton, Scalng & Translaton removal Feature extracton usng DCT Radal Bass Functon Network Verfcaton Database consstng of 800 samples Feature extracton usng DCT Fg.2: Block dagram of proposed system Frst, new mage, also known as user mage s made to undergo Rotaton-Scalng-Translaton removal, so that t resembles orgnal sgnature stored n database. Database s created by collectng 800 sgnature samples. These sgnatures are stored n database after feature extracton process for whch DCT s employed. It s also appled on corrected user mage. Now ths mage s provded to Radal Bass Functon Network (RBFN), whch s earler traned usng database. If RBFN dentfes user mage as the one n database then access for that partcular ndvdual s granted. The advantage of ths system s that t uses neural network effectvely,.e., t requres only few samples to tran the network and then t performs for sgnatures newly added n the database. The algorthm used n ths paper uses correlaton to detect the rotaton and mplements smple croppng method to elmnate the effect. RBFN s used for verfcaton purpose. In paper [3], back-propagaton algorthm s used for sgnature recognton. In ths algorthm, neural network s created wth R,G and B pxel values as nput and gray value as output. To calculate the gradent, unsgned char to unsgned nt and float to unsgned char conversons are needed to be performed. Ths ncreases computaton. Also, the network s traned multple tmes so that proper weght matrx s obtaned. Use of RBFN reduces ths need of multple tranng and acheves the goal n few epochs. Ths paper s organzed as follows: Secton II descrbes dea of proposed soluton. Secton III brefs mplementaton steps and secton IV shows expermental results. Secton V provdes concluson.
II. IDEA OF PROPOSED SOLUTION The prmary concern s decdng at what angle the new sgnature devates from the sgnature n database. For ths, concept of correlaton can be mplemented. Correlaton s a statstcal measure, whch refers to a process for establshng whether or not relatonshps exst between two varables [4]. It denotes the value by whch two varable vary. If the value s maxmum then there s maxmum smlarty n the two varables. Consder orgnal sgnature as varable P ( m, n) and new sgnature to be tested as varable Q ( m, n). If P 0 and 0 Q are mean values of P and Q then cross-correlaton r between them s descrbed by followng equaton: r P P )( Q Q ) m n ( mn 0 mn 0 Normalzed cross- correlaton can be used to smplfy examnaton and comparsons of coeffcent values correspondng to the respectve angular values. Mn-max normalzaton s the procedure used to obtan normalzed cross - correlaton [5]. Mn - max normalzaton mantans the relatonshps among the orgnal data values. The normalzaton operaton transforms the data nto a new range, generally [0, 1]. Gven a data set x, such that 1,2, n, the normalzed value followng equaton: (1) ' x mn( x ) x (2) max( x ) mn( x ) ' x s gven by the The second goal s to obtan the translaton assocated wth the mages. Ths can be accomplshed by a smple croppng technque. Intally, the number of rows and columns borderng the sgnature pxels wthn the mage are calculated. The mage devod of these rows and columns s extracted. The result s an mage consstng of only the sgnature pxels. Ths elmnates addtonal background surroundng the mage. Thrd goal s scalng between two mages. For calculaton of the scalng factor, the cropped mages obtaned durng translaton are used. The sze of the orgnal mage dvded by the sze of the user mage gves the scalng rato. tp such that only pen- down samples (.e., when the pen touches the paper) are recorded. Fg. 3: Wacom Bamboo Dgtal Pen Tablet For testng the system, a database was created. It conssted of a set of sgnature samples of 100 people. For each person, there are 9 test samples and 1 tranng sample. The samples are color normalzed. Color normalzaton converts the colored mage nto correspondng grayscale mage. B. Rotaton Elmnaton There s always some tltng of sgnatures when there s no reference lne provded for t. Ths angle of tlt vares from - 60 o to +60 o. Before verfcaton process, the rotaton s neutralzed by algnng the new mage wth orgnal mage. The new mage s then rotated by 5 wthn the range of 60 to +60 n successve teratons. Cross-correlaton values between the reference mage and the new mage are recorded on completon of each teraton of the rotaton process. The maxmum cross-correlaton value refers to the correct angle of rotaton wthn a 5 range, further, after the approxmate angle value s obtaned, +3 or 3 of ths angle can be nspected for maxmum correlaton value whch corresponds to angle of rotaton accurate to up to 1.The user mage s rotated by the negatve of the angle and we get the mage whch s free from rotaton. Fg. 4 gves the flowchart of ths algorthm. A. Sample Acquston & Pre-processng of Images The system mplemented here uses a dgtal pen tablet, namely, WACOM Bamboo shown n fg.3, as the datacapturng devce. The pen has a touch senstve swtch n ts
Orgnal Image Pre-processed orgnal mage Startng from θ = -60 o START Color normalzaton Removal of background New Image User mage Calculaton of normalzed correlaton value Is θ 60 o? Yes No translaton n X and/or Y drecton, havng a maxmum value equal to the wdth or heght of the sgnature canvas respectvely. The soluton s gven by croppng method n whch only the sgnature pxels are extracted. So wth respect to new mage formed, there s no such translaton n X and/or Y drecton. Ths elmnates the effect of translaton. The number of columns from left and number of rows from the bottom, whch contan no black pxels correspondng to the actual sgnature,.e., whch consst solely of mage- background, are counted. These values gve X translaton and Y translaton respectvely. D. Scalng Elmnaton The sze of the sgnature vares accordng to the sze of space. For smaller spaces, the sgnature may be compressed, for larger space, the sgn may be enlarged. Thus, before extracton of features, t s essental that any scalng, f present n the test sample, be removed. Upon croppng both mages, the rato of heght gves Y scalng and rato of wdth gves X scalng. However, to resze the user mage and make t the same sze as the mage, ether of the scalng ratos can be used. For a rotaton range of 60 to +60, heght was observed to vary sgnfcantly as compared to the length. Hence, Y scalng was chosen as the scalng rato. Scalng rato s calculated by the followng equaton: Scalng rato Sze of reference mage Sze of test mage Increment θ by 5 o No Is Correlaton maxmum? Yes Correct the user mage by θ o The user mage s reszed as per the obtaned scalng rato and then sent to the feature extracton segment. E. Combned Rotaton Scalng Translaton It s not dffcult to control the specmens to get pure rotaton, translaton and scalng, n any case, for orgnal sgnature, all the aforementoned components are adusted synchronously. Thus, rotaton, translaton and scalng modfcatons are connected n the same manner. Fg.4: Flowchart of algorthm C. Translaton Elmnaton STOP Smlar to tltng of sgnature, there s one more dffculty whch s translaton. It s the effect whch ntroduces Rotaton revson goes before translaton revson as the expected cause at base left corner lkewse gets rotated and translaton mpacts can't be dspensed wth unless the orgn s rotated back to bottom left as correctly as could be allowed. In ths way, rotaton adustment needs to be performed frst as the scalng proporton computed by the pure scalng strategy s not relable wth scalng degree of the rotated mage.
Hence, the effcency of scalng revson depends, to a substantal degree, on the percentage error obtaned durng rotaton correcton. archtecture of RBFN has proved to drectly mprove tranng and performance of the network [8]-[9]. Fg. 5 depcts archtecture of RBFN. After correctng the angle of rotaton, the user mage preprocessed duplcate s edted to take out translaton and the mage so acqured s an nstance of pure scalng whch has been dscussed prevously. Along these lnes, rotaton scalng translaton cancellaton s performed. F. Feature Extracton usng DCT DCT s one of the most wdely used transform n the used for feature extracton. It nvolves takng the transformaton of the mage as a whole and separatng the relevant coeffcents. DCT performs energy compacton [6]. The DCT of an mage bascally conssts of three frequency components namely low, mddle, hgh each contanng some detal and nformaton n an mage. The low frequency generally contans the average ntensty of an mage whch s the most ntended n FR systems [7]. Mathematcally, the 2D-DCT of an mage s gven by: F( u, v) ( u) ( v) ( u) ( v) N 1 M 1 x0 y0 1 N 2 N u u cos (2x 1) cos (2y 1) f ( x, y) 2N 2M for u, v 0 for u, v 0 (3) where f ( x, y) s the ntensty of the pxel at coordnates ( x, y), u vares from 0 to M-1, and v vares from 0 to N-1, where M N s the sze of mage. G. Radal Bass Functon Network (RBFN) The constructon of RBFN n ts basc from nvolves three layers, vz., nput layer, hdden layer and output layer. All these layers are assgned dfferent roles. The nput layer s formed by sensory unts that connect the network to ts surroundng. The second layer, the hdden layer n entre network, apples nonlnear transformaton from the nput space to hdden space, where dmensonalty of hdden space s larger than that of nput space. Each neuron n hdden layer has a specal type of actvaton functon centered on the center vector of a cluster or subcluster n the feature space so that the functon has non neglgble response for nput vectors close to ts center. Output layer s lnear, producng response of the network to the actvaton sgnals appled to nput layer. Ths partcular Fg. 5: General archtecture of RBFN The learnng process undertaken by an RBFN can be descrbed as follows. The lnear weghts assocated wth the output unt(s) of the network tend to evolve on a dfferent tme scale compared to nonlnear actvaton functons of the hdden unt. Thus as hdden layer's actvaton functons evolve slowly n accordance wth some nonlnear optmzaton strategy; the output layer's weghts adust themselves rapdly through a lnear optmzaton strategy. There are dfferent learnng strateges that can be followed, based on partcular applcaton. It depends on how centers of RBFN are specfed. For ths applcaton of sgnature recognton, approach of supervsed selecton of centers s taken. Several learnng algorthms have been proposed n the lterature for tranng RBF networks [10] [15]. Selecton of a learnng algorthm for a partcular applcaton s crtcally dependent on ts accuracy and speed. In practcal onlne applcatons, sequental learnng algorthms are generally preferred over batch learnng algorthms as they do not requre retranng whenever a new data s receved. In ths approach, centers of RBFN and all other free parameters of the network undergo a supervsed learnng process. It means that RBFN takes ts most generalzed form of error-correcton learnng. The frst step n the development of such a learnng procedure s to defne the nstantaneous value of the cost functon [16]. ξ 1 2 N 2 e 1 (3)
Where N s the sze of tranng sample used for learnng and e s the error sgnal defned by e d d * F ( x ) M 1 G x t (4) C The requrement s to fnd the free parameters, t and 1 (the latter beng related to norm-weghtng matrx C ) so as to mnmze ξ. III. A. Image Database IMPLEMENTATION STEPS TABLE 1 COMBINED ROTATION SCALING TRANSLATION Sample Orgnal Parameters Detected Parameters Rotaton Scalng Rotaton Scalng 1-50 0.54-48 0.58 2-20 0.9-22 0.98 3-10 0.65-8 0.68 4-5 1.43-3 1.47 5 3 1 1 1.2 6 12 0.83 10 0.88 7 15 1.5 17 1.6 8 32 1.78 30 1.84 9 33 0.75 35 0.79 10 42 0.26 40 0.32 For the expermental purpose, a database of 700 samples was created by collectng 10 samples each from 70 people. The network for dentfcaton was created by usng RBFN fewer neurons model. All the mages were used for tranng of the network. One of the mages was processed by usng rotaton-translaton-scalng algorthm. Fg.6 shows some of the sgnatures used n tranng and testng mage database constructed. Fg.6: Some samples from sgnature database The sgnature recognton system presented n ths paper was developed, traned, and tested usng MATLAB 7. The computer was a Wndows 8 machne wth a 2.5 GHz Intel Core I5 processor and 4 GB of RAM. B. Valdaton of technque The preprocessed grayscale mages of sze 8 8 pxels are reformed n MATLAB to form a 64 1 array wth 64 rows and 1 column for each mage. Ths technque s performed on all test mages to form the nput data for testng the recognton system. Smlarly, the mage database for tranng uses 50 mages and forms a matrx of 64 50 wth 64 rows and 50 columns. The nput vectors defned for the RBFN are dstrbuted over a 2D-nput space varyng over [0 255], whch represents ntensty levels of the grayscale pxels. As many as 5 test mages are used wth the mage database for performng the experments. Tranng and testng sets were used wthout any overlappng. IV. EXPERIMENTAL RESULTS A. Combned Rotaton Scalng Translaton Table 1 dsplays result for combned rotaton scalng translaton n orgnal rotaton and scalng and detected rotaton and scalng. TABLE 1 COMBINED ROTATION SCALING TRANSLATION The graphcal plot of error n actual and detected rotaton angle s depcted n fgure 7 below.
Error percentage Error percentage CT Internatonal Journal of Dgtal Image Processng, ISSN 0974 9675 (Prnt) & ISSN 0974 956X (Onlne) 70 60 50 40 B. Recognton usng MATLAB The network was traned usng varable number of samples. The performance graph of network for 700 samples s shown n fgure 12. 30 20 10 0 Sample 1 2 3 4 5 6 7 8 9 10 Fg. 7: Graphcal plot of error n actual and detected rotaton angle The graphcal plot of error n actual and detected scalng parameter can be seen n fgure 8 below. 25 20 15 10 5 Fg. 12: Performance graph of network for 700 samples The test mage gven to network s shown n fgure 13. The network was successfully able to recognze t though the mage was tlted. 0 1 2 3 4 5 6 7 8 9 10 Sample Fg. 8: Graphcal plot of error n actual and detected scalng parameter Fg. 13: User Image Fg.14: Image recognzed Table 2 dsplays convergence rates relatng to dfferent Mean Square Errors (MSE) and spreads. Orgnal Image Fg.10: Image after Rotaton Correcton Fg.11: Image after Scalng Correcton Fg.9: TABLE 2 CONVERGENCE RATES RELATING TO DIFFERENT MEAN SQUARE ERRORS (MSE) AND SPREADS MSE error Spread Iteratons 2.23695e-31 0.5 500 9.48458e-27 0.5 200 0.620313 0.5 100 2.41667 0.5 70 3.67695 0.5 60 5.25875 0.5 50 10.8547 0.5 40 15.4098 0.5 20 49.6859 0.5 10 211.032 0.5 5
For varous numbers of samples n tranng set, the recognton rate of system also vared. It s tabulated n table. TABLE 3 RECOGNITION RATE FOR VARIOUS NUMBERS OF SAMPLES Number of samples Recognton rate 500 71. 34% 400 76.8% 200 80% 100 79.2% 50 72.65% V. CONCLUSION Ths paper presents a novel method to sgnature recognton usng RBFN. The system was evaluated n MATLAB usng an mage database of 700 sgnatures, contanng 70 people and each person wth 10 sgnatures. After tranng for approxmately 200 samples the system acheved a recognton rate of 80%. A reduced feature space substantally reduces the computatonal requrements of the method as compared wth standard DCT feature extracton methods. Ths makes our system well suted for low-cost, real-tme hardware mplementaton. Commercal mplementatons of ths technque do not currently exst. However, t s concevable that a practcal RBFN-based sgnature recognton system may be possble n the future. VI. FUTURE WORK The system s mage-based and can be extended to real tme sgnature recognton also. Wth IOS and Androd based systems, touch screen devces lke tablets, phablets and smartphones can be effectvely used for dgtal sgnature dentfcaton and authentcaton. Genetc algorthm can be utlzed to solve ths problem of verfcaton wth reduced computaton and ncreased effcency. VII. REFERENCES [1] Anl Jan, Arun Ross and Sall Prabhakar, Introducton to Bometrc Recognton, IEEE Transactons on Crcuts and Systems for Vdeo Technology, Vol. 14, No. 1, January 2004, pp. 4-20. [2] Anl Jan, F. D. Gress and S. D. Connell, On-lne sgnature verfcaton, Elsever, Pattern Recognton 35, 2002, pp. 2963-2972. [3] Debnath Bhattacharyya1 and Ta-Hoon Km, Sgnature Recognton usng Artfcal Neural Network, Proc. 9 th WSEAS Internatonal Conference on Computatonal Intellgence, Man-Machne Systems and Cybernetcs (CIMMACS 10), 2010., pp.183-187. [4] R. J. Rummel, "Understandng Correlaton", Honolulu: Department of Poltcal Scence, Unversty of Hawa, 1976. [5] J. Han, M. Kamber, "Data mnng: concepts and technques," Morgan Kaufmann, 2006, pp. 70-72. [6] N. Ahmed, T. Nataraan, K.R. Rao, Dscrete Cosne Transform, IEEE Transactons on Computers, Vol. C-23 (1974) 9093. [7] Dgtal Image Processng, Rafael Gonzalez, Rchard Woods, Edton 3, Pearson Publcatons, 2008. [8] Song, Y.H., Xuan, Q.Y., and Johns, A.T., Protecton Scheme for E H V Transmsson Systems wth Thyrstor Controlled Seres Compensaton Usng Radal Bass Functon Neural Networks, Electr. Mach. Power Syst., 1997, 25, pp. 553 565. [9] Zohre Fashfar and Javad Haddadna, Desgnng an Fuzzy RBF Neural Network wth Optmal Number of Neuron n Hdden Layer & Effect of Sgnature Shape for Persan Sgnature Recognton by Zernke Moments and PCA, Journal of Computatonal Informaton Systems, September, 2010, 2010 Bnary Informaton Press. [10] L. Yngwe, N. Sundararaan, and P. Saratchandran, A sequental learnng scheme for functon approxmaton usng mnmal radal bass functon (RBF) neural networks, Neural Computat., vol. 9, pp. 461 478, 1997. [11] S. Chen, C. F. N. Cowan, and P. M. Grant, Orthogonal least squares learnng algorthm for radal bass functon networks, IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302 309, Mar. 1991. [12] J. Platt, A resource-allocatng network for functon nterpolaton, Neural Computat., vol. 3, pp. 213 225, 1991. [13] N. B. Karayanns and G. W. M, Growng radal bass neural networks: Mergng supervsed and unsupervsed learnng wth network growth technques, IEEE Trans. Neural Netw., vol. 8, no. 6, pp. 1492 1506, Nov. 1997. [14] M. Salmerón, J. Ortega, C. G. Puntonet, and A. Preto, Improved RAN sequental predcton usng orthogonal technques, Neurocomput., vol. 41, pp. 153 172, 2001. [15] I. Roas, H. Pomares, J. L. Berner, J. Ortega, B. Pno, F. J. Pelayo, and A. Preto, Tme seres analyss usng normalzed PG-RBF network wth regresson weghts, Neurocomput., vol. 42, pp. 267 285, 2002. [16] Smon Haykns, "Neural Networks A Comprehensve Foundaton", Second Edton, Pearson Educaton Inc., 2005, pp.278-327.
Ankt R. Chadha (M 2010) was born n Mumba (M.H.) n Inda on November 07, 1992. He s currently pursung hs undergraduate studes n the Electroncs and Telecommuncaton Engneerng dscplne at Vdyalankar Insttute of Technology Mumba. Hs specal felds of nterest nclude Image Processng, Computer Vson (partcularly, Pattern Recognton) and Embedded Systems. He has 6 papers n Internatonal Journals & Conferences to hs credt. Neha S. Satam (M 2010) was born n Mumba (M.H.) n Inda on November 26, 1992. She s currently pursung her undergraduate studes n the Electroncs and Telecommuncaton Engneerng dscplne at Vdyalankar Insttute of Technology, Mumba. Her felds of nterest nclude Image Processng, Stegnography and Embedded systems. She has 6 papers n Internatonal Journals & Conferences to her credt. Prof. Vbha S. Wal (M 1997) has done B.E. n Electroncs n 1997 from Shva Unversty and Masters n Electroncs and Telecommuncaton from Mumba Unversty. She had taught subects lke computer graphcs, obect orented programmng, data structure, Image processng and many more. She s currently workng as Assstant professor n Vdyalankar college of Technology, Wadala n Informaton Technology department and presently teachng subects lke Electromagnetc Wave Theory, Electromagnetc Engneerng, Neural network and Fuzzy Logc and Rado Frequency Crcut Desgn. Her area of nterest s Image processng and Fractal Image compresson. She has guded numerous undergraduate proects and papers.