A new hybrid CNN–LSTM model with Non-Softmax functions for face spoof detection

Abstract

With the rapid technological development of video transmission, it is applied on various applications such as security, forgery detection and surveillance. Moreover, the images and videos supply a huge volume of variations in intra-personal and also make the face recognition significant in a biometric security area. In addition, automatic face recognition process is a widely applied concept in security. The spoofing attacks account for reproducing a human face by applying photographs or videos. The face recognition and spoof detection processes are performed by using machine learning and deep learning algorithms by analysing the images in videos. For the purpose of enhancing the prediction accuracy, we propose a new hybrid deep learning technique called hybrid convolutional neural network (CNN)-based architecture with long short-term memory (LSTM) units to study the impact in classification. This hybrid approach uses a non-softmax function for making effective decision on classification. The experiments have been performed for evaluating the proposed technique and proved better than the existing deep learning algorithms in terms of precision, recall, f-measure and accuracy.