Abstract
This paper presents a real-time system designed for visually impaired to aid them in social interactions. This system implemented a facial recognition algorithm which when embedded in a wearable device helps to detect the known and unknown faces at any crowded location or gathering. Our aim is to build a fully functional portable assistant that can recognize known faces, objects as well as the text from books. This system acts as the third eye for the blind. The facial recognition algorithm explained in this paper is the initial step towards this approach. Building such a prototype is a very challenging task as the portable camera is subjected to blur images due to the motion of the object and noise in uncertain lighting conditions. So, this paper presents an approach which is resilient to any change in illumination and is trained to detect the faces from the database with high accuracy. The two-tier architecture followed in this approach first identifies presence of a person and then applying face recognition to detect its identity. The object detection part helps in differentiating objects like photo frames and posters from a person and thus, proves more reliable than a standard face recognition framework. A dataset of 1000 images are taken for each face to train the model and system achieves a detection accuracy of 93.2% at a very high frame rate.
R. Goyal and K. Kalra—Contributed equally to this work.
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References
WHO.: Visual impairment and blindness (2014). http://www.webcitation.org/6YfcCRh9L
Jhonny, G.P., Carlos, V.A., Luis, S.A., Eduardo, P.V.: Special glasses for obstacle detection with location system in case of emergency and aid for recognition of dollar bills for visually impaired persons. In: Healthcare Innovations and Point of Care Technologies (HI-POCT), 2017 IEEE, pp. 68–71. IEEE (2017)
Ishikiriyama, J., Suzuki, K.: An interactive virtual mirror to support makeup for visually impaired persons. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 1393–1398. IEEE (2017)
Lu, C., Adluru, N., Ling, H., Zhu, G., Latecki, L.J.: Contour-based object detection using part bundles. Comput. Vis. Image Underst. 114(7), 827–834 (2010)
Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Moving obstacle detection in highly dynamic scenes. In: IEEE International Conference on Robotics and Automation, 2009, ICRA 2009, pp. 56–63. IEEE (2009)
Moreno, M., Shahrabadi, S., José, J., du Buf, J.H., Rodrigues, J.M.: Real-time local navigation for the blind: detection of lateral doors and sound interface. Proc. Comput. Sci. 14, 74–82 (2012)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3476–3483. IEEE (2013)
Zhang, C., Zhang, Z.: A survey of recent advances in face detection (2010)
Ortiz, E.G., Becker, B.C.: Face recognition for web-scale datasets. Comput. Vis. Image Underst. 118, 153–170 (2014)
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Hum. Genet. 7(2), 179–188 (1936)
Raghavendra, R., Rao, A., Kumar, G.H.: Multimodal person verification system using face and speech. Proc. Comput. Sci. 2, 181–187 (2010)
Pong, K.H., Lam, K.M.: Multi-resolution feature fusion for face recognition. Pattern Recogn. 47(2), 556–567 (2014)
Stallkamp, J., Ekenel, H.K., Stiefelhagen, R.: Video-based face recognition on real-world data. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Gorodnichy, D.O.: Video-based framework for face recognition in video. In: Proceedings of the 2nd Canadian conference on computer and robot vision, pp. 330–338. IEEE (2005)
Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Sokolsky, M.: Towards fully autonomous driving: systems and algorithms. In: Intelligent Vehicles Symposium (IV), 2011 IEEE, pp. 163–168. IEEE (2011)
Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vision 73(1), 41–59 (2007)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments, vol. 1, no. 2, p. 3. Technical Report 07-49, University of Massachusetts, Amherst (2007)
Ding, C., Choi, J., Tao, D., Davis, L.S.: Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(3), 518–531 (2016)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001. CVPR 2001, vol. 1, p. 1. IEEE (2001)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. arXiv preprint, 1612 (2016)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Shan, C., Gritti, T.: Learning discriminative LBP-histogram bins for facial expression recognition. In: BMVC, pp. 1–10, September 2008
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Goyal, R., Kalra, K., Kumar, P., Kaur, S. (2018). Intelligent Face Recognition System for Visually Impaired. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_12
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