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Merging face and finger images for human identification

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Abstract

Human identification performance reported so far using face or finger images under certain conditions is good practice, however, there is still a great need for better performance in biometrics for use in video surveillance. One possible way to achieve improved performance is to combine information from multiple sources. Besides, such systems alleviate some of the problems that are faced by single biometrics-based systems like restricted degrees of freedom, spoof attacks, and unacceptable error rates. We present a prototype bimodal biometric identification system by merging face and finger images. A novel approach is adopted to merge biometric (face and finger) traits of an individual to one image (containing features of both), named merged pattern. The integrated features are then extracted with an adaptive artificial neural network. The proposed algorithm is shown to exhibit robustness in achieving better classification results with both good generalization performance and a fast training/test time using variable public domain databases. Sources of variability include facial expression, gender, individual appearance, tilt, lighting conditions, and occluding objects (hair, spectacles, etc).

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References

  1. Jain AK, Prabhakar S, Hong L, Pankanti S (2000) Filterbank-based fingerprint matching. IEEE Trans Image Processing 9(5):846–859

    Article  Google Scholar 

  2. Jain AK, Ross A (2002) Learning user-specific parameters in a multibiometric system. IEEE Int Conf Image Processing (ICIP’02) I:57–60

    Google Scholar 

  3. Frischholz RW, Dieckmann U (2000) BioID: a multimodal biometric identification system. IEEE Comput 33(2): 64–68

    Google Scholar 

  4. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Patterns Anal Mach Intell 20(3):226–239

    Article  Google Scholar 

  5. Hong L, Jain AK, Pankanti S (1999) Can multibiometrics improve performance? In: Proceedings of the AutoID’99 Summit (NJ), USA, pp 59–64

  6. Chang K, Bower KW, Sarkar S, Victor B (2003) Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans Patterns Anal Mach Intell 25(9):1160–1165

    Article  Google Scholar 

  7. Chellappa R, Wilson C, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–739

    Article  Google Scholar 

  8. Nagaty KA (2001) Fingerprints classification using artificial neural networks: a combined structural and statistical approach. Neural Netw 14:1293–1305

    Article  CAS  PubMed  Google Scholar 

  9. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Patterns Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  10. Wei S (1998) Face recognition, final project report, (http://www.cim.mcgill.ca/~wsun/sa/project/report.html)

  11. Valentin A, Abdi H (1996) Can a linear autoassociator recognize faces from new orientations. J Opt Soc Am 13(4):717–724

    Google Scholar 

  12. Gross M (1995) Visual Computing. Springer, Berlin Heidelberg New York, p 289

    Google Scholar 

  13. Lawrence S, Lee GC, Tsoi AC, Andrew AB (1997) Face recognition: a convolutional neural network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  14. Lin SH, Kung SY, Lin LJ (1997) Face recognition/detection by probabilistic decision-based neural network. IEEE Trans Neural Netw 8(1):114–132

    Article  Google Scholar 

  15. Ben-Arie J, Nandy DA (1998) Volumetric/iconic frequency domain representation for objects with application for pose invariant face recognition. IEEE Trans Patterns Anal Mach Intell 20(5):449–457

    Article  Google Scholar 

  16. Kin-Man L, Hong Y (1998) An analytic-to-holistic approach for face recognition based on a single frontal view. IEEE Trans Patterns Anal Mach Intelligence 7(20):673–686

    Article  Google Scholar 

  17. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation, Institute for Cognitive Science Technical Report 8506, UCSD, La Jolla, CA 92093

  18. Sutton R (1986) Two problems with backpropagation and other steepest-descent learning procedures for networks. In: 8th Conference on the cognitive science society, Erlbaum, Hillsdale NJ, pp 823–831

  19. Huang WY, RP (1987) Lippmann, Neural nets and traditional classifiers. In: Anderson DZ (ed) Neural Inf Proces Syst. American Institute of Physics, NY, pp 387–396

    Google Scholar 

  20. Hush DR, Salas JM, Horne B (1991) Error surfaces for multi-layer perceptrons. In: international IEEE joint conference on Neural Networks Seattle NY 1: 759–764

  21. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, England

    Google Scholar 

  22. Feitosa RQ, Thomaz CE, Veigo A, (1999) Comparing the performance of the discriminant analysis and RBF neural network for face recognition. In: 5th international conference Inf systems on analysis and synthesis 6:50–56

  23. Tolba AS, Abu-Rezq AN (2000) Combined classifiers for invariant face recognition. Patterns Anal App 3(4):289–302

    Article  Google Scholar 

  24. Ross A, Jain A (2003) Information fusion in biometrics. Patterns Rec Lett 24:2115–2125

    Article  Google Scholar 

  25. Kohonen T (1995) Self-organizing maps. Springer, Berlin Heidelberg New York

    Google Scholar 

  26. Chibelushi CC, Deravi F, Mason JSD (1999) Adaptive classifier integration for robust pattern recognition. IEEE Trans Syst Man Cybern B: Cybern 29(6):902–907

    Article  Google Scholar 

  27. Su Q, Silsbee PL (1996) Robust audiovisual integration using semi-continuous hidden Markov models. In: 4th international conference spoken language Proceedings 1:42–45

  28. Wagner T, Dieckmann U (1994) Multi-sensorial inputs for the identification of persons with synergetic computers. In: 1st IEEE international conference image processing 2:287–291

  29. Dasarathy BV (1991) Decision fusion strategies in multisensor environments. IEEE Trans Syst Man Cybern 21(5):1140–1154

    Article  Google Scholar 

  30. Khuwaja GA (2002) LVQ base models for recognition of human faces. Int J of Comp App in Tech 16(4):181–193

    Google Scholar 

  31. Robertson G, Craw I (1994) Testing face recognition systems. Image Vis Comput 12:609–614

    Article  Google Scholar 

  32. Craw I, Costen N, Kato T, Akamatsu S (1999) How should we represent faces for automatic recognition. IEEE Trans Patterns Anal Mach Intell 21(8):725–736

    Article  Google Scholar 

  33. Tolba AS (1998) Kuwait University (KU) face database, http://www.sc.Kuniv.edu.kw/lessons/9503587/kufb.htm

  34. Samaria AS (1994) Face recognition using hidden markov models, PhD Dissertation. Trinity College, University of Cambridge

  35. The Psychological Image Collection at Stirling (PICS), nott-faces-originals. http://pics.psych.stir.ac.uk/

  36. Khuwaja GA, Abu-Rezq AN (2003) Data acquisition and recognition of fingerprints with LVQ. Int J Comput Intell App 3(1):65–88

    Google Scholar 

  37. Wang S, Lee C (1999) Fingerprint recognition using directional micropattern histograms and LVQ networks. Int Conf Inf Intell Syst 300–303

  38. Er MJ, Wu S, Lu J, Toh HL (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3): 697–710

    Article  Google Scholar 

  39. Phillips PJ, Grother P, Micheals R, Blackburn DM, Tabassi E, Bone M (2002) Face recognition vendor test. In: IEEE international work analysis and modelling of faces and gestures (AMFG’03)

  40. Blackburn DM, Bone JM, Phillips PJ (2001) FRVT 2000 evaluation report. (http://www.frvt.org/DLS/FRVT_2000.pdf)

  41. Adler A (2002) Automatic face recognition system architecture for collaborative virtual environments. In: IEEE international eork haptic virtual environment and their applications (HAVE), pp 1–6

  42. Chien J, Wu C (2002) Discriminant wavelet faces and nearest feature classifiers for face recognition. IEEE Trans Patterns Anal Mach Intell 24(12):1644–1649

    Article  Google Scholar 

  43. Hagen S (1995) Face Recognition—a novel technique. Master Thesis. University of Heidelberg

  44. Khuwaja GA (2003) Adaptive LVQ classifier for invariant face recognition. Cybern Syst Int J 34(8):725–746

    Google Scholar 

  45. Kawagoe M, Tojo A (1984) Fingerprint pattern classification. Pattern Recogn 17(3):295–303

    Article  Google Scholar 

  46. Karu K, Jain AK (1996) Fingerprint classification. Pattern Recogn 29(3):389–404

    Article  Google Scholar 

  47. Jain AK, Hong L, Bolle (1997) R On-line fingerprint verification. IEEE Trans Patterns Anal Mach Intell 19(4):302–314

    Article  Google Scholar 

  48. Wahab A, Chin SH (1998) Novel approach to automated fingerprint recognition. IEE Proc Vis Image Signal Processing 145(3): 160–166

    Article  Google Scholar 

  49. Emiroglu I, Akhan MB (1997) Pre-processing of fingerprint images. IEE European Conference Security and Detection, pp 147–151

  50. Jiang X, Yau W, Ser W (2001) Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge. Patt Rec 34: 999–1013

    Article  Google Scholar 

  51. Costello BD, Gunawardena CA, Nadiadi YM (1994) Automated coincident sequencing for fingerprint verification. IEE Colloq Image Processing Biom Meas 3:1–5

    Google Scholar 

  52. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) FVC2000: Fingerprint verification competition. IEEE Trans Patterns Anal Mach Intell 24(3): 402–412

    Article  Google Scholar 

  53. Watson C I, Wilson C L (1992) NIST special database 4, fingerprint database, US National Institute of Standards and Technology

  54. Watson C I (1993) NIST special database 14, fingerprint database, US National Institute of Standards and Technology

  55. Jain AK, Bolleand R, Pankanti S (1999) Biometrics-personal identification in networked society. Kluwer, Dordrecht

    Google Scholar 

  56. Lee CJ, Wang SD (1999) Fingerprint feature extraction using Gabor filters. Electron Lett 35(4):288–290

    Article  Google Scholar 

  57. Lee CJ, Wang SD, Wu KP (2001) Fingerprint recognition using principal Gabor basis function. In: Proceedings of international symposium intell multimedia video speech processing, Hong Kong, 393–396

  58. Hong L, Jain A (1998) Integrating faces and fingerprints for personal identification. IEEE Trans Patterns Anal Mach Intell 20(12): 1295–1307

    Article  Google Scholar 

  59. Chatzis V, Bors AG, Pitas I (1999) Multimodal decision-level fusion for person authentication. IEEE Trans Syst Man Cybern 29(6):674–680

    Google Scholar 

  60. Salah AA, Alpaydin E, Akarun LA (2002) Selective attention-based method for visual pattern recognition with application to handwritten digit recognition and face recognition. IEEE Trans Patterns Anal Mach Intell 24(3):420–425

    Article  Google Scholar 

  61. Zhang D, Peng H, Zhou J, Pal SK (2002) A novel face recognition system using hybrid neural and dual eigenspaces methods. IEEE Trans Syst Man Cybern 32(6):787–793

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Physics Department, Kuwait University, P.O. Box 5969, Safat, 13060, Kuwait

    Gulzar A. Khuwaja

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  1. Gulzar A. Khuwaja
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Correspondence to Gulzar A. Khuwaja.

Biographical data

Biographical data

Gulzar Ali Khuwaja received his B.E. (bachelor of engineering) degree in electronic engineering from the Mehran University of Engineering and Technology, Pakistan, in 1988. He received his M.Sc. and Ph.D. degrees from the University of Manchester Institute of Science and Technology (UMIST), England, in 1990 and 1992, respectively. From 1993 to 1996, he was an assistant professor of computer engineering at the British Universities Programme, Pakistan. Since 1997, he has been an assistant professor in engineering physics in digital systems department, Kuwait University.

His research interests include neural networks, pattern recognition, digital signal processing, multimedia data compression, digital systems design, CAD, and modern logic design. He has published 30 papers in these areas.

Dr. Khuwaja was the recipient of The Heaviside Premium award for the best paper of the year 1992/93 sponsored by the IEE Council, UK. He is a member of the IEE (UK) and PEC (Pakistan).

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Khuwaja, G.A. Merging face and finger images for human identification. Pattern Anal Applic 8, 188–198 (2005). https://doi.org/10.1007/s10044-005-0255-4

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