Efficient transfer learning for robust face spoofing detection
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Biometric systems are synonym of security. However, nowadays, criminals are violating them by presenting forged traits, such as facial photographs, to fool their capture sensors (spoofing attacks). In order to detect such frauds, handcrafted methods have been proposed. However, by working with raw data, most of them present low accuracy in challenging scenarios. To overcome problems like this, deep neural networks have been proposed and presented great results in many tasks. Despite being able to work with more robust and high-level features, an issue with such deep approaches is the lack of data for training, given their huge amount of parameters. Transfer Learning emerged as an alternative to deal with such problem. In this work, we propose an accurate and efficient approach for face spoofing detection based on Transfer Learning, i.e., using the very deep VGG-Face network, previously trained on large face recognition datasets, to extract robust features of facial images from the Replay-Attack spoofing database. An SVM is trained based on the feature vectors extracted by VGG-Face from the training images of Replay database in order to detect spoofing. This allowed us to work with such 16-layered network, obtaining great results, without overfitting and saving time and processing.