Download - ijcer

More documents

Recommendations

Info

Literature review: Iris Segmentation Approaches for Iris Recognition Systems F. Eight-neighbor connection based clustering An efficient as well as robust algorithm for noisy iris image segmentation in the background of non cooperative and less-cooperative iris recognition has been developed in this. The major contributions involved in this are as follows. Firstly, a novel region growing scheme known as the eight-neighbor connection based clustering [6] has been proposed in order to cluster the whole iris image into different parts. Then, genuine iris region has been extracted with the aid of several semantic priors, and also the non-iris regions such as eyelashes, eyebrow, glass frame, hair, etc are identified and also excluded as well, which intensely reduces the possibility of mis localizations occurring on the non-iris regions. Secondly, an integrodifferential constellation has been introduced inorder to accelerate the traditional integrodifferential operator, and then, enhance its global convergence ability for pupillary as well as the limbic boundary localization. Thirdly, a 1-D horizontal rank filter as well as an eyelid curvature model has been adopted in order to tackle the eyelashes as well as the shape irregularity, during eyelid localization. Finally, the eyelash as well as the shadow occlusions has been detected with the aid of learned prediction model which is based on the intensity statistics between different iris regions. The advantage of this method is that the iris segmentation accuracy has been attained. The drawback is segmentation of noisy iris images should be improved. All these techniques have improved the performance of the iris recognition system. All these techniques acquire segmentation accuracy in many areas such as boundary detection, iris detection, pupil and limbic boundary detection etc. But none of these papers provide a solution for attaining overall segmentation accuracy. Thus there is an extreme need for developing a new segmentation method, which is more reliable as well as robust for automatically segmenting the non ideal iris images. This paper introduces a unified solution for iris segmentation [9]. Here, a new iris segmentation framework has been developed, which can robustly segment the iris images attained using NIR or else the visible illumination. This approach exploits multiple higher order local pixel dependencies using Zernike moments (ZM), in order to strongly (robustly) classify the eye region pixels into iris or else the non iris regions using trained neural network/ support vector machine (NN/SVM) classifiers. Image enhancement using single scale retinex (SSR) algorithm has been employed for illumination variation problem. Face as well as the eye detection modules has been integrated in the unified framework to automatically provide the localized eye region from facial image for segmenting the iris. A robust post processing operations algorithm has been developed in order to effectively alleviate the noisy pixels which has been caused due to misclassification. NAME APPROACH PERFORMANCE DISADVANTAGES High confidence visual recognition of persons by a test of statistical independence Integrodifferential operator High performance in iris recognition Computational time is very high Iris Recognition: An emerging biometric technology Hough transform Segmentation accuracy achieved up to an extent Does not provide attention to EL as well as reflections etc Recognition of Human Iris Patterns for Biometric Identification Liber Masek’s encoding algorithm Localization of circular iris region as well as eyelids, eyelashes and also the reflections occurs Speed of the system is low Iris segmentation methodology for non cooperative recognition Fuzzy clustering algorithm Better segmentation for non cooperative iris recognition Thorough search is needed to recognize the circle parameters of both pupil and iris boundaries Toward Accurate and Fast Iris Segmentation for Iris Biometrics Pushing and pulling (PP) method Possess accuracy and speed Occurrence of segmentation error Efficient and robust segmentation of noisy iris images for non cooperative iris recognition Eight-neighbor connection based clustering iris segmentation accuracy has been attained to an extent Table1: Comparison table on literature survey segmentation of noisy iris images should be improved III. CONCLUSION This paper presents a literature survey on the various segmentation techniques involved in iris recognition. There are various techniques that can be used for this purpose. Overall segmentation accuracy of all these techniques has been analyzed. Higher the segmentation rate, thus higher is its performance. The unified framework [7] has the highest segmentation rate and also has highest performance. IV. ACKNOWLEDGMENT This work is supported and guided by my research guide. I am very thankful to my research guide Ms. Jini. K. M, Assistant Professor, Computer Science Department, Nehru college of Engineering and Research Centre, India for her guidance and support. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 69
Literature review: Iris Segmentation Approaches for Iris Recognition Systems REFERENCES [1] J.G. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, p.1148, Nov 1993 [2] R.P. Wildes, Iris recognition: An emerging biometric technology, Proceedings of the IEEE, 85 p.1348, sep. 1997 [3] L. Masek, “Recognition of Human Iris Patterns for Biometric Identification”, M.S. Dissertation, The University of Western Australia, 2003 [4] H. Proença, L.A. Alexandre, “Iris segmentation methodology for non-cooperative recognition”, IEE Proceedings of Vision, Image and Signal Processing, pp. 199-205, 2006 [5] Z. He, T. Tan, Z. Sun, and X. Qiu, “Toward accurate and fast iris segmentation for iris biometrics,” IEEE Trans. Pattern Anal. Machine Intelligence, vol. 31, no. 9, pp. 1670–1684, Sep. 2009. [6] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition,” Image Vis. Comput., vol. 28, no. 2, pp. 223–230, Feb. 2010. [7] Tan, C. and Kumar, A., Unified framework for automated iris segmentation using distantly acquired face images. IEEE Transactions on Image Processing, 21(9):4068–4079, Sep 2012 www.<strong>ijcer</strong>online.com ||May ||2013|| Page 70
Page 1 and 2:
International Journal of computatio
Page 3 and 4:
Meisam Mahdavi Qualification: Phd E
Page 5 and 6:
12. 13. 14. 15. 16. 17. 18. 19. 20.
Page 7 and 8:
15. 16. 17. 18. 19. 20. Traditional
Page 9 and 10:
19. A Study on Security in Sensor N
Page 11 and 12:
International Journal of Computatio
Page 13 and 14:
Study Of The Energy Potential Of So
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Neural network approach to power sy
Page 21 and 22:
Neural network approach to power sy
Page 23 and 24:
Strength, Corrosion correlation of
Page 25 and 26:
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
Mutual Funds and SEBI Regulations S
Page 33 and 34:
REFERENCES Mutual Funds and SEBI Re
Page 35 and 36:
Integral solution of the biquadrati
Page 37 and 38:
Integral solution of the biquadrati
Page 39 and 40:
Experimental Studies on Effect of C
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Simulation studies on Deep Drawing
Page 49 and 50:
Page 51 and 52:
Radial strain Radial Strain Hoop St
Page 53 and 54:
Page 55 and 56:
Text Extraction in Video II. MAIN C
Page 57 and 58:
Text Extraction in Video good idea.
Page 59 and 60:
Text Extraction in Video Fig: 5.1 T
Page 61 and 62:
Improved Performance for “Color t
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Chain code based handwritten cursiv
Page 69 and 70:
Chain code based handwritten cursiv
Page 71 and 72:
Strength of Ternary Blended Cement
Page 73 and 74:
Strength of Ternary Blended Cement
Page 75 and 76:
Page 77 and 78:
Ni-Based Cr Alloys and Grain Bounda
Page 79 and 80:
Page 81 and 82:
Comparative Study of Available Tech
Page 83 and 84:
Comparative Study of Available Tech
Page 85 and 86:
Analyzing massive machine data main
Page 87 and 88:
Analyzing massive machine data main
Page 89 and 90:
Implementation Of An Algorithmic To
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
Page 97 and 98:
Model Order Reduction By Mixed... a
Page 99 and 100:
Amplitude The unit time step respon
Page 101 and 102:
Enterprise Management Information S
Page 103 and 104:
Enterprise Management Information S
Page 105 and 106:
A Novel Approach For Filtering Unre
Page 107 and 108:
A Novel Approach For Filtering Unre
Page 109 and 110:
Current Routing Strategies To Adapt
Page 111 and 112:
Energy Based Routing Protocol for W
Page 113 and 114:
Energy Based Routing Protocol for W
Page 115 and 116:
Cfd Analysis of Convergent- Diverge
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128: Observation on the Ternary Cubic Eq
Page 129 and 130: (2) n n 3n n x (2 ,1) y(2 ,1) 3[
Page 131 and 132: Observation on the Ternary Cubic Eq
Page 133 and 134: Integral Solutions of the Homogeneo
Page 135 and 136: Integral Solutions of the Homogeneo
Page 137 and 138: International Journal of Computatio
Page 145 and 146: Dividing expression (a) by (b), P =
Page 147 and 148: Highly Efficient Motorized…. Torq
Page 149 and 150: Highly Efficient Motorized…. = =
Page 153 and 154: A comparative study of Broadcasting
Page 155 and 156: A comparative study of Broadcasting
Page 157 and 158: Recommendation Systems: a review Co
Page 159 and 160: Recommendation Systems: a review 1.
Page 161 and 162: Recommendation Systems: a review V.
Page 163 and 164: Using Fast Fourier Extraction Metho
Page 171 and 172: P= Number of Poles. d) Fourth Objec
Page 173 and 174: Using Genetic Algorithm Minimizing
Page 175 and 176: Using Genetic Algorithm Minimizing
Page 177: Literature review: Iris Segmentatio
Page 181 and 182: Effects of Agreement on Trims on In
Page 187 and 188: A Novel Approach to Mine Frequent I
Page 189 and 190: A Novel Approach to Mine Frequent I
Page 191 and 192: Time(ms) A Novel Approach to Mine F
Page 195 and 196: Similarly, reactions at bearings (1
Page 197 and 198: Modeling and Analysis of the Cranks
Page 201 and 202: Efficient Model for OFDM based IEEE
Page 209 and 210: Traditional Uses Of Plants By The T
Page 211 and 212: Traditional Uses Of Plants By The T
Page 215 and 216: Content Based Video Retrieval Using
Page 217 and 218: Content Based Video Retrieval Using
Page 219 and 220: Effective and secure content retrie
Page 225 and 226: speed rad/sec Master-Slave Speed Co
Page 227 and 228: speed rad/sec speed rad/sec speed r
Page 229 and 230:
Cd-Hmm For Normal Sinus Rhythm 1 0
Page 231 and 232:
Cd-Hmm For Normal Sinus Rhythm III.
Page 233 and 234:
Develop A Electricity Utility… In
Page 235 and 236:
Develop A Electricity Utility… Ex
Page 237 and 238:
Develop A Electricity Utility… Fi
Page 239 and 240:
Page 241 and 242:
Implementing CURE to Address Scalab
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Design of Uniform Fiber Bragg grati
Page 249 and 250:
Design of Uniform Fiber Bragg grati
Page 251 and 252:
Reflectivity (%) Reflectivity (%) D
Page 253 and 254:
Effect of Alkaline Activator on Wor
Page 255 and 256:
comp. strengthin Mpa Comp. strength
Page 257 and 258:
Effect of Alkaline Activator on Wor
Page 259 and 260:
Priority Based Service Composition
Page 261 and 262:
Priority Based Service Composition
Page 263 and 264:
Page 265 and 266:
Formation of pseudo-random sequence
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Block mathematical coding method of
Page 275 and 276:
Block mathematical coding method of
Page 277 and 278:
Page 279 and 280:
Fast Encryption Algorithm for Strea
Page 281 and 282:
Page 283 and 284:
Area and Speed wise superior Vedic
Page 285 and 286:
Area and Speed wise superior Vedic
Page 287 and 288:
Page 289 and 290:
Effect of nano-silica on properties
Page 291 and 292:
Effect of nano-silica on properties
Page 293 and 294:
Page 295 and 296:
Load forecasting for practical powe
Page 297 and 298:
In scalar notation, the fitted mode
Page 299 and 300:
Mape load in MW Load forecasting fo
Page 301 and 302:
Load forecasting for practical powe
Page 303 and 304:
Page 305 and 306:
Efirstaid medical services for acci
Page 307 and 308:
Page 309 and 310:
Comparative study of capacitance of
Page 311 and 312:
Comparative study of capacitance of
Page 313 and 314:
Increasing the Comprehensibility of
Page 315 and 316:
Increasing the Comprehensibility of
Page 317 and 318:
Page 319 and 320:
Smart Message Communication for War
Page 321 and 322:
Page 323 and 324:
In Mobile Ad hoc Networks: Issues a
Page 325 and 326:
In Mobile Ad hoc Networks: Issues a
Page 327 and 328:
Page 329 and 330:
Comparison Of UPS Inverter Using PI
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Ageing Behaviour of Epdm/Pvc Compos
Page 339 and 340:
Ageing Behaviour of Epdm/Pvc Compos
Page 341 and 342:
Page 343 and 344:
On Α Locally Finite In Ditopologic
Page 345 and 346:
Page 347 and 348:
A Study On Security In Sensor… IE
Page 349 and 350:
A Study On Security In Sensor… Th
Page 351 and 352:
Page 353 and 354:
Design and Simulation of Nonisolate
Page 355 and 356:
Page 357 and 358:
Page 359 and 360:
Page 361 and 362:
Evaluating The Privacy Measure Of T
Page 363 and 364:
Evaluating The Privacy Measure Of T
Page 365 and 366:
Work Done On Avoidance… n New RA
Page 367 and 368:
Work Done On Avoidance… Probabili
Page 369 and 370:
Data Aggregation Protocols In Wirel
Page 371 and 372:
Page 373 and 374:
Page 375 and 376:
Page 377 and 378:
Development Of Virtual Backbone…
Page 379 and 380:
Development Of Virtual Backbone…
Page 381 and 382:
Page 383 and 384:
Domain Driven Data Mining: An… To
Page 385 and 386:
Domain Driven Data Mining: An… X
Page 387 and 388:
Domain Driven Data Mining: An… [5
Page 389 and 390:
The Rhythm Of Omission Of Articles
Page 391 and 392:
All of them are from Rural backgrou
Page 393 and 394:
Page 395 and 396:
Page 397 and 398:
Page 399 and 400:
Page 401 and 402:
Image Segmentation using RGB… gra
Page 403 and 404:
Image Segmentation using RGB… and
Page 405 and 406:
Image Segmentation using RGB… [8]
Page 407 and 408:
Performance Comparison Of Rayleigh
Page 409 and 410:
Page 411 and 412:
Page 413 and 414:
Page 415 and 416:
Walking the talk in training future
Page 417 and 418:
Page 419 and 420:
Page 421 and 422:
Page 423 and 424:
Experimental Investigation Of Multi
Page 425 and 426:
Page 427 and 428:
Page 429 and 430:
A Firm Retrieval Of Software… Cla
Page 431 and 432:
A Firm Retrieval Of Software… hop
Page 433 and 434:
A Firm Retrieval Of Software… tab
Page 435 and 436:
2.1. Introduction to Service Matchi
Page 437 and 438:
Reduced Complexity Of Service Match
Page 439 and 440:
Page 441 and 442:
Pulsed Electrodeposition Of Nano…
Page 443 and 444:
Pulsed Electrodeposition Of Nano…
show all

Download - ijcer

Create successful ePaper yourself

Delete template?

Save as template?