12-07-2014, 03:15 PM
Secure atm by imageprocessing
Secure atm.pptx (Size: 102.58 KB / Downloads: 13)
INTRODUCTION:
This paper proposes an ATM security model that would combine a physical access card ,a pin and electronic facial recognition. It encloses the information regarding the ‘ image processing’. And discussed one of the major application of image processing ‘biometrics’. Biometrics technology turns your body in to your password. We will discuss various biometric techniques like finger scan, retina scan, facial scan, hand scan etc. . Face recognition technology may solve the problem since a face is connected to its owner making it impenetrable system.
AUTOMATED TELLER MACHINE:
An automated teller machine (ATM) is a computerized telecommunications device that provides the customers of a financial institution with access to financial transactions in a public space without the need for a human clerk or bank teller. On most modern ATMs, the customer is identified by inserting a plastic ATM card with a magnetic stripe or a plastic smartcard with a chip, that contains a unique card number and some security information , such as an expiration date. Security is provided by the customer entering a personal identification number (PIN).
BIOMETRICS:
A biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual’s identity. Biometrics can measure both physiological and behavioral characteristics. Physiological biometrics -based on measurements and data derived from direct measurement of a part of the human body. Behavioral biometrics -based on measurements and data derived from an action.
Types of Biometric
Finger-scan biometrics is base on the distinctive characteristics of the human fingerprint. Fingerprints are used in forensic applications: large- scale, one-to-many searches on databases of up to millions of fingerprints.
INPUT PROCESSING:
INPUT PROCESSING A pre-processing module locates the eye position and takes care of the surrounding lighting condition and colour variance. First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and Normalization process may be required to bring the dimensions of the live facial sample in alignment with the one on the template.
CONCLUSION:
CONCLUSION With new improved techniques like ARTIFICIAL INTELLIGENCE security margin can be increased from simple 60-75% to 80-100% We thus develop an ATM model that is more reliable in providing security by using facial recognition software. By keeping the time elapsed in the verification process to a negligible amount we even try to maintain the efficiency of this ATM system to a greater degree making it faster and impenetrable