Iris Presentation Attack Detection (PAD) is critical for securing recognition systems, yet its practical deployment is severely hindered by the poor generalization of models across different acquisition devices and diverse datasets. To address this persistent cross-domain challenge, we first introduce a comprehensive evaluation framework, the Iris Presentation Attack Detection Cross-Domain-Testing (IPAD-CDT) Protocol, designed to evaluate the model robustness in these scenarios. Our core contribution is a novel Masked Mixture-of-Experts (MMoE) method, which enhances the generalization of Transformer-based architectures. MMoE introduces a structured information asymmetry, where "student" Experts learn robust features from masked inputs by distilling knowledge from an unmasked "teacher" Expert via a cosine distance loss. This mask-and-distill mechanism effectively mitigates overfitting and guides the model to learn domain-invariant cues. By integrating MMoE into a CLIP-based model, we conduct extensive experiments on our IPAD-CDT protocol. The results demonstrate that our method sets a new state-of-the-art, significantly outperforming existing models, especially in the challenging cross-dataset and cross-device settings.

Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separately due to their distinct characteristics. Thus, when being combined attacked, almost all methods could not deal. Some studies attempt to combine the sparse data from both types of attacks into a single dataset and try to find a common feature space, which is often impractical due to the space is difficult to be found or even non-existent. To overcome these challenges, we propose a novel approach that uses the sparse model to handle sparse data, utilizing different parameter groups to process distinct regions of the sparse feature space. Specifically, we employ the Mixture of Experts (MoE) framework in our model, expert parameters are matched to tokens with varying weights during training and adaptively activated during testing. However, the traditional MoE struggles with the complex and irregular classification boundaries of this problem. Thus, we introduce a flexible self-adapting weighting mechanism, enabling the model to better fit and adapt. In this paper, we proposed La-SoftMoE CLIP, which allows for more flexible adaptation to the Unified Attack Detection (UAD) task, significantly enhancing the model’s capability to handle diversity attacks. Experiment results demonstrate that our proposed method has SOTA performance.

The cosmetic contact lenses over an iris may change original iris textural pattern which is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. For practical application scenes, the cosmetic contact lenses detection still facing unsolved problems, due to the low image quality and difficulty in accurately iris localization. In this paper, we propose a novel framework called Weighted Region Network (WRN) to detect the cosmetic contact lenses. The WRN includes a local attention Weight Network and a global classification Region Network. With the inherent attention mechanism, the proposed network is able to find more discriminative regions, which reduces the requirement for target detection and improves the ability of classification. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several public databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.

With the development of iris recognition, many identity authentication applications began to use this inherent biometric ID. Despite the breakthroughs in the identification with iris recognition technology, one primary problem remains unsolved: the presentation spoof attack. In this paper, we present a novel algorithm 4DCycle-GAN for expanding the spoof iris image database by synthesizing fake iris images wearing textured contact lenses. The proposed 4DCycle-GAN follows the Cycle-Consistent Adversarial Networks (Cycle-GAN) framework which translating between one kind images (genuine iris images) and one other kind images (textured contact lenses iris images). The 4DCycle-GAN introduces two more discriminators to improve the Cycle-GAN at the defect of lack of diversity. The two new discriminators `prefer' images generated by the generators, while the original discriminators in Cycle-GAN `prefer' real captured images. These new added confrontations make the 4DCycle-GAN avoid generating a certain kind of contact lenses texture which is larger percentage of the training iris database. The synthesized textured contact lenses iris images are used for spoofing iris detection training to improve the robustness of classification algorithm. Both the Cycle-GAN and the 4DCycle-GAN synthesizing images can improve the spoof classification results. Moreover, by using the 4DCycle-GAN, the spoof classification results are distinctly improved for unrelated non-homologous database experiments. Extensive experimental results show that the proposed method can improve the anti-spoof ability of iris recognition system.

The remarkable Deep Deterministic Policy Gradient (DDPG) reinforcement learning method commonly consists of actor learning and critic learning. The actor learning highly relies on the critic learning, which makes the performance of DDPG method rather sensitive to critic learning and leads to stability issues. To further improve the stability and performance of DDPG method, the multi-critic DDPG method (MCDDPG) is proposed for a reliable critic learning. The average value of multiple critics is used to replace the single critic in DDPG method for better resistance when one critic performs badly, and multiple independent critics can learn knowledges from environment more widely. Besides, an extension of experience replay mechanism is revealed for accelerating the training process. All the methods are tested on simulated environments in OpenAI Gym platform, and convincing experiment results are obtained to support the proposed methods.

Nowadays, we get more and more remote sensing (RS) images which cannot be well processed or used by manual analysis or existing automatic methods. In the past few years, the object detection technology has greatly developed, especially after the usage of CNN in object detectors. However, Object detection in large scale RS images is still a challenging tasks which needs further study. Compared to natural images, RS images include much more objects in different sizes with a larger scope. Therefore, it is extraordinarily time-consuming to detect small objects in a large-scale RS image, since this work needs more scale and location traverses. Algorithms for common images cannot tackle the problem of some special object detection, like aircraft detection, in RS images. In this paper, we introduce an extra Region Proposal strategy named Region Locating Network (RLN) to improve the Faster RCNN framework. The proposed RLN locates spectacular areas where aircrafts are usually found, like parts of the runway and the parking apron. Based on the locating result, we can use Faster RCNN to detect airplanes in several smaller image regions. Extensive experiments show that the proposed method has oblivious improvement in recall rate, accuracy and computing efficiency.

Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

Eye detection is a hot research topic in computer vision for its wide applications in human-computer interaction, face and iris recognition, etc. However, robust eye detection is still a grand challenge due to the numerous appearance variations of eye images in real-world applications. In this paper, we present a novel local surface curvature analysis method to deal with this problem. Firstly, by regarding an eye image as a 2D surface in 3D space, we propose to use the histogram of local surface curvatures as the general representation of eye pattern. Then, a SVM classifier is employed for eye detection using the histogram vectors of eye and non-eye samples. Extensive experiments are performed and the results show that the proposed method achieves state-of-the-art performance in eye detection. In particular, it is more efficient in mistakenly localized iris detection.

Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged. Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper, we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified SIFT descriptor is extracted at each pixel of the image. Secondly, the SIFT descriptor is used to rank the LBP encoding sequence. Then, statistical features are extracted from the weighted LBP map. Lastly, SVM classifier is employed to classify the genuine and counterfeit iris images. Extensive experiments are conducted on a database containing more than 5000 fake iris images by wearing 70 kinds of contact lens, and captured by four iris devices. Experimental results show that the proposed method achieves state-of-the-art performance in contact lens spoof detection.