A wearable face recognition system on Google Glass for assisting social interactions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9010, Issue , 2015, Pages 419-433

  Mandal, Bappaditya ^a   Chia, Shue Ching ^a   Li, Liyuan ^a   Chandrasekhar, Vijay ^a   Tan, Cheston ^a   Lim, Joo Hwee ^a  

^a INSTITUTE FOR INFOCOMM RESEARCH   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DISCRIMINANT ANALYSIS; EXTRACTION; FEATURE EXTRACTION; GLASS; LIGHTING; SMARTPHONES; SOCIAL SCIENCES; USER INTERFACES; WEARABLE COMPUTERS; WEARABLE TECHNOLOGY;

FACE FEATURE EXTRACTION; FACE RECOGNITION SYSTEMS; INDEPENDENT SYSTEMS; LIGHTING CONDITIONS; PERSONAL INFORMATION; SOCIAL INTERACTION SCENARIOS; SOCIAL INTERACTIONS; SUBCLASS DISCRIMINANT ANALYSES (SDA);

FACE RECOGNITION;

EID: 84942525056     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16634-6_31     Document Type: Conference Paper

References (41)

1. Krishna, S., Little, G., Black, J., Panchanathan, S.: A wearable face recognition system for individuals with visual impairments. In: ACM SIGACCESS Conference on Computer and Accessbility, pp. 106–113 (2005)
2. Utsumi, Y., Kato, Y., Kunze, K., Iwamura, M., Kise, K.: Who are you?: a wearable face recognition system to support human memory. In: ACM Proceedings of the 4th Augmented Human International Conference, pp. 150–153 (2013)
3. Singletary, B.A., Starner, T.E.: Symbiotic interfaces for wearable face recognition. In: In HCII2001 Workshop on Wearable Computing (2001)
4. H.F., E., Haan, D., Campbell, R.: A fifteen year follow-up of a case of developmental prosopagnosia. Cortex, a Journal Devoted to the Study of the Nervous System and Behaviour 27 (1991) 489–509
5. Bate, S.: Face recognition and its disorders. Palgrave Macmillan, New York (2013)
6. Kennerknecht, I., Grueter, T., Welling, B., Wentzek, S., Horst, J., Edwards, S., Grueter, M.: First report of prevalence of non-syndromic hereditary prosopagnosia (hpa). Am. J. Med. Genet. 140, 1617–1622 (2006)
7. Grter, T., Grter, M., Carbon, C.: Neural and genetic foundations of face recognition and prosopagnosia. J. Neuropsychol. 2, 79–97 (2008)
8. Wang, X., Zhao, X., Prakash, V., Shi, W., Gnawali, O.: Computerized-eyewear based face recognition system for improving social lives of prosopagnosics. In: Proceedings of the 7th International Conference on Pervasive Computing Technologies for Healthcare, pp. 77–80 (2013)
9. Nametag (2014). http://www.nametag.ws/
10. OpenCV: Open source computer vision (2014). http://opencv.org/
11. Yu, X., Han, W., Li, L., Shi, J.Y., Wang, G.: An eye detection and localization system for natural human and robot interaction without face detection. In: Groß, R., Alboul, L., Melhuish, C., Witkowski, M., Prescott, T.J., Penders, J. (eds.) TAROS 2011. LNCS, vol. 6856, pp. 54–65. Springer, Heidelberg (2011)
12. Mandal, B., Jiang, X., Eng, H.L., Kot, A.: Prediction of eigenvalues and regularization of eigenfeatures for human face verification. Pattern Recogn. Lett. 31, 717–724 (2010)
13. Jiang, X.D., Mandal, B., Kot, A.: Face recognition based on discriminant evaluation in the whole space. In: IEEE 32nd International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), pp. 245–248. Honolulu, Hawaii, USA (2007)
14. Zhu, M., Martinez, A.: Subclass discriminant analysis. IEEE PAMI 28, 1274–1286 (2006)
15. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35, 399–458 (2003)
16. Phillips, P.J.: Face & ocular challenges. Presentation: http://www.cse.nd.edu/BTAS10/BTASJonathonPhillipsSep2010FINAL.pdf (2010)
17. Rowden, L., Klare, B., Klontz, J., Jain, A.K.: Video-to-video face matching: establishing a baseline for unconstrained face recognition. In: IEEE BTAS (2013)
18. Jiang, X.D., Mandal, B., Kot, A.: Complete discriminant evaluation and feature extraction in kernel space for face recognition. Mach. Vis. Appl. 20, 35–46 (2009). Springer
19. Grother, P., Ngan, M.: Face recognition vendor test (frvt) performance of face identification algorithms. Technical report (2014). http://biometrics.nist.gov/cslinks/face/frvt/frvt2013/NIST 8009.pdf
20. Beveridge, R., Bolme, D., Teixeira, M., Draper, B.: The csu face identification evaluation system user’s guide: version 5.0. Technical report (2013). http://www.cs.colostate.edu/evalfacerec/data/normalization.html
21. Viola, P., Jones, M.: Robust real-time face detection. IJCV 57, 137–154 (2004)
22. Jiang, X.D., Mandal, B., Kot, A.: Eigenfeature regularization and extraction in face recognition. IEEE PAMI 30, 383–394 (2008)
23. Mandal, B., Jiang, X.D., Kot, A.: Dimensionality reduction in subspace face recognition. In: IEEE ICICS, pp. 1–5 (2007)
24. Liu, W., Wang, Y., Li, S.Z., Tan, T.N.: Null space approach of fisher discriminant analysis for face recognition. In: ECCV, pp. 32–44 (2004)
25. Liu, W., Wang, Y.W., Li, S.Z., Tan, T.N.: Null space-based kernel fisher discriminant analysis for face recognition. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 369–374 (2004)
26. Mandal, B., Jiang, X.D., Kot, A.: Multi-scale feature extraction for face recognition. In: IEEE International Conference on Industrial Electronics and Applications (ICIEA), pp. 1–6 (2006)
27. Mandal, B., Jiang, X.D., Kot, A.: Verification of human faces using predicted eigenvalues. In: 19th International Conference on Pattern Recognition (ICPR), pp. 1–4. Tempa, Florida, USA (2008)
28. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)
29. Swets, D.L., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE PAMI 18, 831–836 (1996)
30. Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognit. 33, 1771–1782 (2000)
31. Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE PAMI 19, 696–710 (1997)
32. Wang, X., Tang, X.: A unified framework for subspace face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1222–1228 (2004)
33. Chen, L.F., Liao, H.Y.M., Ko, M.T., Lin, J.C., Yu, G.J.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognit. 33, 1713–1726 (2000)
34. Mandal, B., Eng, H.L.: Regularized discriminant analysis for holistic human activity recognition. IEEE Intell. Syst. 27, 21–31 (2012)
35. Mandal, B., Eng, H.L.: 3-parameter based eigenfeature regularization for human activity recognition. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 954–957 (2010)
36. Mandal, B., Zhikai, W., Li, L., Kassim, A.: Evaluation of descriptors and distance measures on benchmarks and first-person-view videos for face identification. In: International Workshop on Robust Local Descriptors for Computer Vision, ACCV (2014)
37. Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained video with matched background similarity. In: IEEE CVPR, pp. 529–534 (2011)
38. Hastie, T., Tibshirani, R.: Discriminant analysis by gaussian mixtures. J. Roy. Stat. Soc. Ser. B 58, 155–176 (1996)
39. Gkalelis, N., Mezaris, V., Kompatsiaris, I.: Mixture subclass discriminant analysis. IEEE Signal Process. Lett. 18, 319–322 (2011)
40. Mandal, B., Ching, S., Li, L.:Werable device database (2014). https://drive.google.com/folderview?id=0B2veiY3G5XZxVkd3eC1SdEROaGM&usp=sharing
41. Google: Google glass (2014). http://www.google.com/glass/start/