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 http://en.wikipedia.org/wiki/Karhunen- Lo%C3%A8ve_transform ICA(Independent Component Analysis) http://en.wikipedia.org/wiki/Independent_component_analysis http://en.wikipedia.org/wiki/Independent_component_analysis LDA(Linear Discriminant Analysis) : http://en.wikipedia.org/wiki/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 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 with Dual 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 of input 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 ConditionFloating pointInteger PCA + 3 kinds of classEER 14.79%EER 14.81% PCA + NIR neutral images EER 12.65%EER 12.66%

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 ConditionRateRate with neutral image LDA15.32%13.19% ICA14.78%12.62% Integer point PCA14.81%12.66% PCA,NIR imagesEER 12.65%EER 12.66%

17. 4. Experiment Result cont. 4.5 The recognition Performance Using Integer-based PCA, LDA and ICA Count. ConditionEER rateProcessing Time in desktop PC Processing Time in PDA ICA14.78% (12.62%) 82.23 ms556 ms Integer point ICA19.1% (17.08%)34 ms230 ms

18. 4. Experiment Result cont. 4.5 Comparative processing time on

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/contenuT xt/Demos/demo29/face_finderfake.html PCA (Principal Component Analysis): http://en.wikipedia.org/wiki/Karhunen- Lo%C3%A8ve_transform http://en.wikipedia.org/wiki/Karhunen- Lo%C3%A8ve_transform MPEG ( La Baule ): http://www.chiariglione.org/mpeg/meeti ngs/labaule/labaule_press.htm