The Classification Approach for Face Spoof Detection in Artificial Neural Networks Based on IoT Concepts

Abstract

Face spoof detection and authentication systemhas significant role in IoT application that include validation of identity for computer security atm access crime detection and social care. Even though the authentication and classification system are vulnerable to different types of attacks. Presentation attack is a clear threat for facial and biometric based security and authentication applications. The discussed issues can be solved using artificial neural networks for face proof detection in IoT platform. The deep learning approaches for feature extraction in multicolor space is useful for obtaining more information from the input face regarding chrominance and luminance data. The extracted features can be selected and combined with minimum redundancy maximum relevance algorithm for providing discriminate and efficient feature set.