A practical face recognition system demands not only high recognition performance but also the capability of detecting spoofing attacks. While emerging approaches to face antispoofing have been proposed in recent years, most of them perform poorly on unseen samples. The generalizability of face antispoofing needs to be significantly improved before it can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.g., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials makes the spoofing attacks quite different. Another reason for the poor generalizability is that limited labeled data are available for training for face antispoofing. We focus on improving the generalizability of convolutional neural network (CNN)-based face antispoofing methods across different kinds of datasets. We propose a deep domain transfer CNN using sparsely unlabeled data from the target domain to learn features that are invariant across domains for face antispoofing. Experiments on five face spoofing datasets show that the proposed method significantly improves the cross-test performance only with a small number of unlabeled samples from the target domain.
To bridge the gap between two different distributions, many approaches have been proposed based on the ideas of domain adaptation @cite_12 @cite_30 @cite_6 @cite_17 . Multimodal deep learning architectures have been proposed to learn domain invariant representations in @cite_2 . However, this method performed primarily in a generative context and did not leverage the full representation power of supervised CNN representations. In @cite_3 , proposed pre-training with a denoising auto encoder, then train a two-layer network simultaneously with the MMD domain confusion loss. Nevertheless, the learned network is relatively shallow and therefore lacks the strong semantic representation which is learned by directly optimizing with a supervised deep CNN. In @cite_6 , proposed a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss for classification. They use MMD both to select the depth and width of the architecture while using it as a regularizer during fine-tuning, and achieved state-of-the-art performance on the standard visual domain adaptation benchmark.
@cite_30 @cite_6 @cite_3 @cite_2 @cite_12 @cite_17
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Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task. Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings. Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias. Fine-tuning deep models in a new domain can require a significant amount of labeled data, which for many applications is simply not available. We propose a new CNN architecture to exploit unlabeled and sparsely labeled target domain data. Our approach simultaneously optimizes for domain invariance to facilitate domain transfer and uses a soft label distribution matching loss to transfer information between tasks. Our proposed adaptation method offers empirical performance which exceeds previously published results on two standard benchmark visual domain adaptation tasks, evaluated across supervised and semi-supervised adaptation settings. In this paper, we make two contributions to unsupervised domain adaptation (UDA) using the convolutional neural network (CNN). First, our approach transfers knowledge in all the convolutional layers through attention alignment. Most previous methods align high-level representations, e.g., activations of the fully connected (FC) layers. In these methods, however, the convolutional layers which underpin critical low-level domain knowledge cannot be updated directly towards reducing domain discrepancy. Specifically, we assume that the discriminative regions in an image are relatively invariant to image style changes. Based on this assumption, we propose an attention alignment scheme on all the target convolutional layers to uncover the knowledge shared by the source domain. Second, we estimate the posterior label distribution of the unlabeled data for target network training. Previous methods, which iteratively update the pseudo labels by the target network and refine the target network by the updated pseudo labels, are vulnerable to label estimation errors. Instead, our approach uses category distribution to calculate the cross-entropy loss for training, thereby ameliorating the error accumulation of the estimated labels. The two contributions allow our approach to outperform the state-of-the-art methods by +2.6 on the Office-31 dataset.