Abstract
The modern era is accompanied by various traditional mobility aids which help visually impaired to stay independent and enabling them detecting the objects and scanning surroundings. The use of haptic touch, as well as ultrasound, is embedded in today’s smart canes which detect obstacles up to 3 m distance, GPS navigation, informs the user through Bluetooth and earpiece, and guide the visually impaired to direct from one location to another. The evolution of this technology has motivated the integration of inexpensive camera technology within the cane for facial recognition purposes. The concept of developing this intelligent smart cane which would detect obstacles from up to 10 m as well as recognises friends and family faces, was envisioned by students at Birmingham City University. The developments in this product and adopted technologies guide a visually impaired user to detect obstacles and to find an alternative route while at the same time try to recognize any family or friends within the vicinity. These have been reflected in this research paper along with the limitations and wider issues which may come up when adopting the high-tech advances.
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References
Wagner, A., Wright, J., Ganesh, A., Zhou, Y., Ma, Y.: Toward a practical face recognition system: robust pose and illumination via sparse representation. In: CVPR (2009)
Acquisti, A., Gross, R.: Predicting social security numbers from public data. Proc. Natl. Acad. Sci. 106(27), 10975–10980 (2009)
Tkacik, G., et al.: Natural images from the birthplace of the human eye. PLoS ONE 6, e20409 (2011)
Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88682-2_24
Jia, H., Martinez, A.: Face recognition with occlusions in the training and testing sets. In: FGR (2008)
Jia, H., Martinez, A.: Support vector machines in face recognition with occlusions. In: CVPR (2009)
Hartzog, W., Selinger, E.: I see you: the databases that facial recognition apps need to survive. The Atlantic (2014)
Kim, J., Choi, J., Yi, J., Turk, M.: Effective representation using ICA for face recognition robust to local distortion and partial occlusion. PAMI 27(12), 1977–1981 (2005)
Wright, J., Ma, Y.: Dense error correction via \( {\ell^{1}} \)- minimization. Preprint (2008)
Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. PAMI 31, 210–227 (2009)
Jernigan, C., Mistree, B.: Gaydar: Facebook friendships expose sexual orientation. First Monday 14(10) (2009)
Bowyer, W.K., Chang, K., Flynn, P.: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Comput. Vis. Image Underst. 101, 1–15 (2006)
Zhang, L., Samaras, D.: Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI). 28, 351–363 (2006)
Zhang, L., Shan, S., Chen, X., Gao, W.: Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans. Image Process. 16(1), 57–68 (2007)
Arandjelović, O., Cipolla, R.: A pose-wise linear illumination manifold model for face recognition using video. Comput. Vis. Image Underst. (CVIU) 113, 113–125 (2009)
Arandjelović, O.: Recognition from appearance subspaces across image sets of variable scale. In: Proceedings of the IAPR British Machine Vision Conference (BMVC) (2010)
Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Rev. 57, 1701 (2010)
Phillips, P.J., Newton, E.: Meta-analysis of face recognition algorithms. In: Proceedings of the Fifth International Conference on Automatic Face and Gesture Recognition, p. 235 (2002)
Braje, W.J.: Illumination encoding in face recognition: effect of position shift. J. Vis. 3, 161–170 (2003)
Phillips, P.J., O’Toole, A.J.: Comparison of human and computer performance across face recognition experiments. Image Vis. Comput. 32(1), 74–85 (2014)
Yan, S., Xu, D., Yang, Q., Zhang, L., Tang, X., Zhang, X.J.: Multilinear discriminant analysis for face recognition. IEEE Trans. Image Process. 16(1), 212–220 (2007)
Starr, M.: Facial recognition app matches strangers to online profiles, CNET (2014)
Tsukayama, H.: Facebook facial recognition policy draws attention from German privacy regulator, Washington Post (2013)
Wilmer, J.B., Germine, L., Chabris, C.F., Chatterjee, G., Williams, M., Loken, E.: Human face recognition ability is specific and highly heritable. Proc. Natl. Acad. Sci. 107(11), 5238–5241 (2010)
Wilson, R.R., Blades, M., Pascalis, O.: What do children look at in an adult face with which they are personally familiar? Br. J. Dev. Psychol. 25, 375–382 (2007)
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Majeed, A., Baadel, S. (2016). Facial Recognition Cane for the Visually Impaired. In: Jahankhani, H., et al. Global Security, Safety and Sustainability - The Security Challenges of the Connected World. ICGS3 2017. Communications in Computer and Information Science, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-319-51064-4_32
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DOI: https://doi.org/10.1007/978-3-319-51064-4_32
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