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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]

Highlights: [•] A novel method to model the relationship between attributes. [•] An effective method to exploit the learned relationship for model training. [•] A single framework adapted to both discrete and continuous attributes. [•] Promising results for face verification and object recognition. [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]