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Card less ATM Using Deep Learning and Facial Recognition Features

Sahil Bajaj, Sumit Dawda, Pradnya Jadhav, Rasika Shirude

Automated Teller Machine (ATM) is now used for transactions by different users in day to day life due to its high convenience. With the help of ATM’s, banking is now easier. But there have been a lot of frauds that are occurring in ATM’s. Thus there is a need to provide more security to these ATM’s. The proposed system provides the real-time detection and real-world encounter through Haar Cascade & CNN, an analytical service. The software starts by taking pictures of everyone and keeps the details in a database. The proposed activity works with the default detection system. The method consists of three stages, the first is facial detection from the image, and the second is obtaining all the facial details for the purpose of expression recognition to detect liveness. The most useful features that differ from the camera image are extracted from the feature extraction section finding out if all the face details are visible. This feature vector creates an active face representation. In the third step our feature background is designed to find out what an osculated face looks like