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A Summary of Face Recognition Based on Near-Infrared Light Using Mobile Phone by Song- yi Han

A Summary of Face Recognition Based on Near-Infrared Light Using Mobile Phone by Song- yi Han. Korea Univ. Parallel Algorithm Lab. By Hong Seung-woo Friday, July 25, 2007. Abstract. A trend is to adopt biometric technology in mobile phones.

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A Summary of Face Recognition Based on Near-Infrared Light Using Mobile Phone by Song- yi Han

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  1. A Summary of Face Recognition Based on Near-Infrared Light Using Mobile Phone by Song-yi Han Korea Univ. Parallel Algorithm Lab. By Hong Seung-woo Friday, July 25, 2007

  2. Abstract • A trend is to adopt biometric technology in mobile phones. • New NIR(Near-Infra-Red) lighting face recognition • 1. New eye detection method • 2. A simple logarithmic image enhancement method • 3. Integer-based PCA (Principal Component Analysis) method • 4. Proof better performance with integer-based method

  3. 1. Introduction • Fingerprint recognition phone have not become popular yet because it request a DSP chip and a sensor. • In other to most of phone were adopted a built-in mega-pixel camera

  4. 1. Introduction cont. • Programs of using face recognition on mobile phone. • Lack of a NIR ( Near-Infra-Red) lighting to detect eye. • Confusing at detecting where indoor or outdoor for lighting normalization • From a learning-based face/eye detection method to a integer-based face recognition method • More high accuracy of eye detection algorithm with glasses face.

  5. 2. Proposed Face Detection and Recognition

  6. 2. 1 Face Localization based on the Corneal SR (Specular Reflection)

  7. 2. 1 Face Localization based on the Corneal SR (Specular Reflection) cont. • When the result was lack of threshold to each method , get more five images • Th1 = 4 (MBA), Th2 = 70 (OBA), Th3 = 50 (ELA) ( we obtained they as the threshold by experiment. ) • SR is detected using A2 and A4 with based A3 by outdoor sunlight. • To be down-sampled as 30*30 pixels

  8. 2.2 Lighting Normalization • We need a homomorphic filter through FFT( fast Fourier Transform) processing ( based on the floating point operation). • New FFT Using lookup table of logarithmic equation, the complexity is O(0) becoming great.

  9. 2.3 Face Recognition by Using the Integer-Based PCA Method • PCA (Principal Component Analysis) : http://en.wikipedia.org/wiki/Karhunen-Lo%C3%A8ve_transform • ICA(Independent Component Analysis) http://en.wikipedia.org/wiki/Independent_component_analysis • LDA(Linear Discriminant Analysis) : http://en.wikipedia.org/wiki/Linear_discriminant_analysis • EER( Equal Error Rate ) : the rate that is same FAR and FRR. • FRR( False Rejection Rate) : • FAR(False Acceptance Rate) : • Near-infrared (NIR, IR-A):http://en.wikipedia.org/wiki/Infrared

  10. 4. Experiment Result • Integer-based oriented PCA (Principal Component Analysis) method for face recognition.

  11. 4. Experiment Result cont. • Environment of Experiment • Samsung SCH-770(3072*2304) 7 Mega-pixel withDual NIR illuminators(wavelength of 830nm) • Z-distance is 30~40cm • 350 images form 50 classes.(4,1,1,1)

  12. 4. Experiment Result cont. • 4.1 The accuracy of Eye Detection Algorithm • Six categories ofinput dates: • contact lens, eyeglasses, without that. (223lux) • contact lens, eyeglasses, without that. (1,394lux) • The Successful eye detection rate • 99% without eyeglasses • 98.8% with eyeglasses

  13. 4. Experiment Result cont. • 4.2 The performance of Proposed Brightness Normalization Method based on logarithmic equation (see Sect. 2.2) • EER • 14.79% with BNM • 16.43% without it

  14. 4. Experiment Result cont. • 4.3 The face recognition Accuracy using NIR images • A class is several kinds of face image is one smile, one surprise ,one frown and neutral • When we measured with NIR images or MPEG database, the accuracies were almost same with the class images.

  15. 4. Experiment Result cont. • 4.4 The Recognition Accuracy Using Floating-Point PCA

  16. 4. Experiment Result cont. • 4.5 The recognition Performance Using Integer-based PCA, LDA and ICA • So, NIR face image was fetter than that of LDA and ICA

  17. 4. Experiment Result cont. • 4.5 The recognition Performance Using Integer-based PCA, LDA and ICA Count.

  18. 4. Experiment Result cont. • 4.5 Comparative processing time on 3 times faster

  19. 5. Conclusion and Future Work • Achievement • New NIR lighting face recognition method apt for mobile phones. • Future work • Test our algorithm on more mobile phones. • Combine face and iris recognition with more field tests

  20. References • [1]:http://www.idiap.ch/pages/contenuTxt/Demos/demo29/face_finderfake.html • PCA (Principal Component Analysis): http://en.wikipedia.org/wiki/Karhunen-Lo%C3%A8ve_transform • MPEG ( La Baule ): http://www.chiariglione.org/mpeg/meetings/labaule/labaule_press.htm

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