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Abstract: In this paper, we present a novel approach to deal with the problem of detecting whether the eyes in a given still face image are closed, which has wide potential applications in human–computer interface design, facial expression recognition, driver fatigue detection, and so on. The approach combines the strength of multiple feature sets to characterize the rich information of eye patches (concerning both local/global shapes and local textures) and to construct the eye state model. To further improve the model׳s robustness against image noise and scale changes, we propose a new feature descriptor named Multi-scale Histograms of Principal Oriented Gradients (MultiHPOG). The resulting eye closeness detector handles a much wider range of eye appearance caused by expression, lighting, individual identity, and image noise than prior ones. We test our method on real-world eye datasets including the ZJU dataset and a new Closed Eyes in the Wild (CEW) dataset with promising results. In addition, several crucial design considerations that may have significant influence on the performance of a practical eye closeness detection system, including geometric normalization, feature extraction, and classification strategies, are also studied experimentally in this work. [Copyright &y& Elsevier]

Abstract: Eye localization has gained a wide range of applications in face recognition, gaze estimation, pose estimation, expression analysis, etc. However, due to the high degree of appearance variability of eyes in size, shape, color, texture and various ambient environment changes, this task is challenging. During the last three decades, numerous techniques have been developed to meet these challenges. The goal of this paper is to categorize and evaluate these algorithms in a comprehensive way. We focus on the overall difficulties and challenges in real-life scenarios, and present a detailed review of prominent algorithms from the perspective of learning generalizable, flexible and efficient statistical eye models from a small number of training images. In addition, we organize the discussion of the global aspects of eye localization in uncontrolled environments, towards the development of a robust eye localization system. This paper concludes with several promising directions for future research. [Copyright &y& Elsevier]

The goal of cross-modal hashing is to map data from several modalities into a compact Hamming space for efficient and accurate retrieval. Despite the satisfactory performance, existing approaches are reliant on the closed-world assumption. When confronted with real-world retrieval tasks involving out-of-distribution (OOD) semantic data, the similarity relationships of known data retained in hash codes tend to be disrupted by these unknown ones, resulting in retrieval performance degradation. To this end, we present a deep self-enhancing hashing (DSEH) method, simultaneously learning multi-level similarity-preserved hash codes of the known multi-label cross-modal data and robustness to OOD instances. Specifically, we propose to construct pseudo-OOD samples in the feature space using random linear combinations to explore OOD semantics, during the training process. Meanwhile, a prototype-based generative model is incorporated to aggregate batch data to enhance the data representation's differences in known and unknown semantics. Furthermore, we describe a bounded cosine quadrupled loss with distance bound to preserve the multi-level similarity of multi-label data and control the maximum distance between known data and the minimum distance between known and pseudo-OOD data for learning OOD robustness. Extensive experiments show that the DSEH achieves state-of-the-art performance on closed-world tasks and good performance on simulated real-world tasks. • The non-convex random linear combinations of features help construct pseudo-OOD data. • Features produced by the prototype-based auto-encoder enhance the OOD discriminative. • Constraining the maximum distance of dissimilar codes improves the OOD robustness. [ABSTRACT FROM AUTHOR]

Phasor measurement units (PMUs) have been integrated into the smart grid for monitoring the operational state of system and improving the reliability. Due to the high cost of PMU installment, the optimal placement strategies have attracted considerable attention in the literature. However, the impacts of cyber threats on the placement have been largely ignored owing to the cyber complexities. This paper initializes the study on the optimal PMU placement in a smart grid under the cyber threats. A probabilistic model is developed for assessing the unobservable risk of the power grid. We characterize the impacts of several cyber factors on the PMU placements including the number of directly attacked PMUs, the dependence among attack outcomes, and risk propagation. We further study the impacts of cyber attacks on the allocation strategies under a bi-level placement model. In particular, a novel 'greedy' algorithm for PMU placement is introduced with the presence of cyber risks. Our studies show that the cyber risk can significantly increase the unobservability risk of a power system which in turn requires additional PMU allocations, and the dependence among cyber attacks can lead to more unobservable risk. [ABSTRACT FROM AUTHOR]