iosr-JECE   Volume-8 ~ Issue-2

Abstract: A Real-Time accident prevention system has been proposed in which the drowsy condition of the driver can be detected and Speed Controlling will be performed for each condition automatically. There are different ways to detect drowsiness one of them are using camera that points directly towards the driver's face and monitors the driver's eyes in order to detect fatigue. We have developed a drowsy driver detection system using Brain Computer Interface ,the system deals with EEG Signal obtained from the brain ,when rhythms are plotted on PC we can see the fluctuations of rhythms when subject is falling to drowsy or deep sleep in accordance with a appropriate voltage under normal condition and drowsy condition are read on software application, using these voltage under two states we have developed a alert system and speed controlling for drowsy driver .However, the current BCI system is developed to detect the drowsiness ,cognitive state and when drowsy state occurs a warning tone is employed to alert him from the drowsy state, in some cases when driver don't respond to the warning tone, the speed of vehicle rapidly reduced and finally the vehicle will be stopped.

Keywords : Brain Computer Interface,Cognitive,Drowsy,EEG,Speed Controlling.

[1] Brown ID. "Driver fatigue and road safety. Alcohol, Drugs and Driving" 1993.

[2] J.D Wu, T.R Chen "Development of a drowsiness warning system based on the fuzzy logic images analysis" Expert Systems with Applications 34 (2008), pp. 1556–1561.

[3] Brown ID "Driver fatigue Human Factors", 1994. pp:298-314.

[4] G.Maycock. "Sleepiness And Driving : Experience of UK Cars Drivers" vol 5 1996.

[5] L.A.Rayner ,J.A.Horne. 23 June 1997."Falling asleep whilst driving drivers aware of prior sleepiness.

[6] S. Makeig and M. Inlow, "Lapses in alertness: Coherence of fluctuations in performance and EEG spectrum," Electroencephalography Clinical Neurophysiol., vol. 86, no. 1, pp. 23–35, 1993.

[7] W. Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis," Brain Res. Rev., vol. 29, pp. 169–195, 1999.

[8] M. A. Schier, "Changes in EEG alpha power during simulated driving " ,2000.

[9] Jim Horne and Louise Reyner, Sleep Related Vehicle Accidents, Sleep Research Laboratory, Loughborough University, 2000.

[10] G.E. Birch and S.G. Mason. Brain-Computer Interface Research at the Neil Squire Foundation, IEEE Trans. Rehab. Eng., 8(2), 193-95, 2000.

Abstract: In this paper we will discuss the methods of iris recognition method and present their simulation results. Start from the novel segmentation method which is based on use of Geodesic Active Counters (GAC) in order to extract the iris from surrounding structures. Further for the features extraction and localization, we have used the well known method called Gabor filters. At the end for the matching we have used normalized correlation-based iris matching method. This proposed matching system is a fusion of global and local Gabor phase correlation schemes. We have measured performance in terms of false acceptance rate, false rejection rate and compared against our previous approach of iris recognition using MATLAB.

Keywords: Human iris pattern, Iris Segmentation, Geodesic Active Counters, Gabor filters, False Acceptance Rate, False Rejection Rate.

[1] D. M. Monro, S. Rakshit, and Z. Dexin, "DCT-based iris recognition,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 586– 595,APR 2007.

[2] R. P.Wildes, "Iris recognition: An emerging biometric technology," Proc. IEEE, vol. 85, no. 9, pp. 1348–1363, Sep. 1997.Apr. 2007.

[3] N.A. Schmid, M.V. Ketkar, H. Singh, and B.Cukic, "PerformanceAnalysis of Iris- Based Identification System at the Matching ScoreLevel," IEEE Trans. Information Forensics and Security, vol. 1, no. 2,pp. 154-168, June 2006.

[4] W.W. Boles, B. Boashash, A human identification technique using images ofthe iris and wavelet transform, IEEE Trans. Signal Process. 46 (4) (1998) 1185–1188.

[5] James R. Matey, Oleg Naroditsky, Keith Hanna, Ray Kolczynski, Dominick J. LoIacono, Shakuntala Mangru, Michael Tinker, Thomas M. Zappia, and Wenyi Y. Zhao, "Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments", Proceedings of the IEEE, Vol. 94, No. 11, pp. 1936 – 1947, November 2006.

[6] S. Shah and A. Ross, "Iris segmentation using geodesic active contours", IEEE Trans. Inf. Forensics Security, vol. 4, no. 4, pp. 824–836, Dec. 2009.

[7] T. Weldon, W.E. Higgins, D.F. Dunn, Efficient Gabor-filter design for texture segmentation, Pattern Recognition 29 (12)(1996) 2005–2016.

Abstract: With rapidly growing network, Internet has become a primary source of transmitting confidential or secret data such as military information, financial documents, etc. In such cases, techniques devoted to protect such kind of information are needed and they play an important role in providing confidential and secure transmission over network. Visual Cryptography is also one of them which is used to hide secret visual information (such as image, text, etc) in which secret sharing scheme is used. Secret sharing is used to encrypt a secret image into customized versions of the original image. There are many secret sharing algorithms in literature including Shamir, Blakley, and Asmuth-Bloom to divide the image into no. of shares. These sharing schemes lead to computational complexity and also generate shares like noisy images. Then afterwards Lin & Tsai proposed a scheme which creates meaningful shares but having same computational complexity as like Shamir's scheme. Along with this, in these schemes, as decryption is done using Human Visual system, the secret can be retrieved by anyone if person get at least k no. of shares. To overcome all above problems, we are suggesting one new method in which a symmetric secret key is used to encrypt the image and then secret shares are generated from this image using Novel secret sharing technique with steganography. So, finally this method will produce meaningful shares and use of secret key will ensure the security of scheme. This scheme can become a reliable solution suitable for today's authentication challenges.

Index Terms: Visual cryptography, Secret sharing, steganography.

[1] Prabir Naskar, Ayan Chaudhuri, Atal Chaudhuri "Image Secret Sharing Scheme Using a Novel Secret Sharing Technique with Steganography ", IEEE CASCOM Post Graduate Student Paper Conference 2010, Kolkata, India,Nov. 27, 2010, pp-62-65.
[2] Satyendra Nath Mandal, Subhankar Dutta and Ritam Sarkar, "Block Based Symmetry Key Visual Cryptography", I. J. Computer Network and Information Security, 2012, 9, pp-10-19.
[3] Feng Liu and Chuankun Wu, Senior Member, IEEE "Embedded Extended Visual Cryptography Schemes" IEEE Transactions on information forensics and security, vol. 6, no. 2, June 2011, pp-307-322.
[4] A. Shamir, "How to share a secret," Proc. Comm. ACM, vol. (2), 612-613, 1979.
[5] G. Blakley, "Safeguarding cryptographic keys," Proc. the National Computer Conference, NJ, USA, 1979.
[6] C. Asmuth and J. Bloom, "A modular approach to key safeguarding", IEEE Transaction on Information Theory, 29(2), 1983, pp-208-210.
[7] C.C. Lin and W.H. Tsai, "Secret image sharing with steganography and authentication", Journal of Systems and software, vol. 73, no. 3, 2004, pp. 405-414.
[8] Kai-Hui Lee and Pei-Ling Chiu, "An Extended Visual Cryptography Algorithm for General Access Structures", IEEE Transactions on information forensics and security, vol. 7, no. 1, February 2012, pp-219-229.
[9] Subramania Sudharsanan, Senior Member, IEEE, "Shared Key Encryption of JPEG Color Images", IEEE Transactions on Consumer Electronics, Vol. 51, No. 4, NOVEMBER 2005, pp-1204-1211.