Face spoofing detection technology can accurately distinguish the truth of the face captured by the camera, and it has been applied in many security fields. In view of the problems of the existing face spoofing detection models, such as the single way to extract image features and the insufficient semantic information, a model architecture that fused the predictive feature maps of different scales was proposed. Firstly, the face image is randomly cropped and the local image is taken as input; Then, a cheap feature predictive convolution attention module is designed to alleviate the problem of feature map redundancy, and make full use of the similarity features of adjacent blocks around local pixels, that is, predict the feature value of the central region through surrounding pixels; Finally, the feature maps extracted from different levels are fused. The model aims to achieve high accuracy in identifying the authenticity of face images. The experiments show that the accuracy rate on CASIA-SURF (Depth modal) dataset is 99.42%, the average classification error rate is 0.53%, and the zero error rate is achieved on CASIA-FASD and Replay-Attack datasets. The model parameter quantity is only 0.37M, which is lower than most of the model architecture based on convolution neural networks.
KEYWORDS: RGB color model, Wavelets, Feature extraction, Education and training, Image fusion, Finite element methods, Data modeling, Performance modeling, Overfitting, Light sources and illumination
Face-spoofing detection plays an important role in ensuring the security of face recognition systems. Most multi-modal methods based on deep learning improve their accuracy by utilizing information from RGB, depth, and infrared. In fact, given the cost and application conditions, it is difficult to obtain all these data. Therefore, it is especially important to exploit single-modal images to extract more detailed information. To address the above problems, we propose an efficient two-stream convolutional network, which takes an original image and its wavelet-transformed image as input. Then, we design two branches to extract the features, with the wavelet branch more conducive to mining the detailed information. Finally, we adopt three loss functions to supervise the two branches and the fused branch respectively, and each branch can be scored separately. The extensive experiments demonstrate that our model can achieve satisfactory performance on the datasets, with replay-attack and CASIA-FASD achieving 100% accuracy.
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