HIIDE MACHINE PASSWORD

Fromm, My smartphone is a safe! The user's point of view regarding novel authentication methods and gradual security levels on smartphones, in: 2010 international conference on security and cryptography (SECRYPT), IEEE, 2010, pp. iPhone 5S fingerprint security can be easily broken, Hackers Show. Derpanis, Integral image-based representations, Department of Computer Science and Engineering York University Paper, 1 (2007) 1-6. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (1993) 1148-1161. Daugman, New methods in iris recognition, IEEE Transactions on Systems, Man, and Cybernetics. Daugman, How iris recognition works, IEEE Transactions on Circuits and Systems for Video Technology, 14 (2004) 21-30. Reynolds, Biometric authentication for mobile devices, in: Proceeding of the 3rd Australian information warfare and security conference, 2002, pp. Furnell, Authenticating mobile phone users using keystroke analysis, International Journal of Information Security, 6 (2007) 1-14.

Furnell, Authentication of users on mobile telephones-A survey of attitudes and practices, Computers and Security, 24 (2005) 519-527.

HIIDE MACHINE PASSWORD

Teh, Secure Encrypted Steganography Graphical Password scheme for Near Field Communication smartphone access control system, Expert Systems with Applications, 41 (2014) 3561-3568. Samsung Galaxy S5 smartphone fingerprint sensor hacked. Alpar, Intelligent biometric pattern password authentication systems for touchscreens, Expert Systems with Applications, 42 (2015) 6286-6294. In the performance, the recognition accuracy was 0.1% EER (equal error rate) and the eye detection rate with the speed of 17.64ms on a mobile phone was 99.4%. As a result, two 850nm LEDs were selected for iris illumination at 1.1cm away from the iris camera for the size of a 7cm 13.7cm phone. The experiments were conducted on a currently released mobile phone and the results showed promising potential for adoption of iris recognition as a reliable authentication means. A fast eye detection algorithm was proposed for implementation under the mobile platform with low computational facility. Subsequently, in view of the disparity between the user's gazing point and the center of the iris camera which causes degradation of acquired iris images, an experiment was performed to locate the appropriate gazing point for good iris image capture. An experimental study was conducted to search for the relevant power and wavelength of NIR LED illuminators with their positioning on a phone for capturing a good quality iris image. This paper raises these issues in detail based on real implementation of an iris recognition system in a mobile phone and proposes some solutions to these issues. The iris recognition on a mobile phone is different from the conventional iris recognition implemented on a dedicated device in that the computational power of a mobile phone and the space for placing NIR (near infrared) LED (light emitting diode) illuminators and iris camera are limited. Address issues from the implementing iris recognition system on mobile phone.Propose optimal wavelength and installation position of illuminators for the system.An optimal user's gazing point is proposed to minimize occlusion and off-angle.Eye detection algorithm can detect an eye in real time on mobile phone.BERC mobile-iris database is constructed autonomously by using a proposed system.

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