A Machine Learning and IoT-based Anti-spoofing Technique for Liveness Detection and Face Recognition

Security systems used in public and corporate sectors these days are becoming more and more biometrics dependent. Face recognition is one of the most prominent tools used in authentication systems. It used to involve creating a digital template of the face of a person and identifying it, which is a unique physical characteristic for each and every person. But this method of biometrics is susceptible to certain threats. The system can be fooled using the image of the person or by displaying videos or photos of the person in front of the camera. To overcome this problem, certain methods have been proposed, which include texture analysis, frequency analysis, 3D face shaping, etc. The constraint of these methods is that they require high computational powers because of the length and complexity of the codes. These methods are suitable to be implemented on high-end devices, such as smartphones, laptops, etc. The aim of this paper is to provide a method for liveness detection suitable for edge devices. A method that is much more reliable and uses less computational power is proposed. Raspberry pi is used to implement the proposed technique which itself is an edge device. The two major complications of a facial recognition system are tackled separately by using the algorithms of eye blink detection and object detection.