Face alignment has been well studied in recent years, however, when a face alignment model is applied on facial images with heavy partial occlusion, the performance deteriorates significantly. In this paper, instead of training an occlusion-aware model with visibility annotation, we address this issue via a model adaptation scheme that uses the result of a local regression forest (RF) voting method. In the proposed scheme, the consistency of the votes of the local RF in each of several oversegmented regions is used to determine the reliability of predicting the location of the facial landmarks. The latter is what we call regional predictive power (RPP). Subsequently, we adapt a holistic voting method (cascaded pose regression based on random ferns) by putting weights on the votes of each fern according to the RPP of the regions used in the fern tests. The proposed method shows superior performance over existing face alignment models in the most challenging data sets (COFW and 300-W). Moreover, it can also estimate with high accuracy (72.4% overlap ratio) which image areas belong to the face or nonface objects, on the heavily occluded images of the COFW data set, without explicit occlusion modeling.

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Hand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask for a pixel using structured forests. This approach can better exploit hand shape information in the training data, and enforce shape constraints in the estimation. Aggregation of multiple predictions generated from neighboring pixels further improves the robustness of our method. We evaluate our method on both ego-centric videos and unconstrained still images. Experiment results show that our method can detect hands efficiently and outperform other state-of-the-art methods.

Motivation. Despite the great success of recent facial landmarks localization approaches, the presence of occlusions significantly degrades the performance of the systems [2,5]. Though occlusion occur frequently in realistic scenarios (e.g. the use of scarf or sunglasses, hands or hair on the face), very few works have addressed this problem explicitly due to the high diversity of occlusion in real world. While [4] tried to model a few synthetic occlusion patterns, the recent method of [1] dealt with the occlusion problem in more realistic sceneries. Both of them only focused on modelling the occlusion in an unstructured way, i.e. treating the visibility of each landmark independently. However in realistic conditions, the occlusion patterns (or called occluders) often occupy a continuous region instead of an individual pixel location, as depicted in Fig 2. Thereby the whole occluded region will consistently affect the landmarks localization.Contribution. This work attempts to address the face mask reasoning and facial landmarks localization in an unified Structured Decision Forests framework. We first have built a rich face image dataset with face mask annotation. The dataset was built as an extension of the recent datasets: Caltech Occluded Faces in the Wild (COFW), Labeled Face Parts in the Wild (LFPW) and Labeled Face in the Wild (LFW). We manually annotate a portion of images in these datasets with face masks. The face mask indicates whether or not each pixel belongs to the face. Then we incorporate such additional information of dense pixel labelling into training the Structured Classification-Regression Decision Forest. The classification nodes aim at decreasing the variance of the pixel labels of the patches by using our proposed structured criterion while the regression nodes aim at decreasing the variance of the displacements between the patches and the facial landmarks. The proposed framework allows us to predict the face mask and facial landmarks locations jointly. The proposed framework with following properties. First, semi-supervised, it uses training images from the above described augmented dataset, only a portion of which are with face masks. Second, structured, it has a novel structured criterion for split function selection for the pixel labelling (face mask reasoning) problem. Third, joint classification-regression, it predicts face mask label for each pixel (classification) and the landmark locations (regression) at the same time, and more importantly it uses the face mask reasoning results Figure 1: The framework of proposed method. We use face images with annotation of facial landmarks and face masks for training. By randomly switching the information gain function at the internal nodes, the decision trees are optimized with respect to both the offsets to landmarks (regression) and to the local structured label configuration (classification). The forest model is able to predict the face mask and landmark locations jointly. We exploit the face mask prediction to further improve the landmar...

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We present a novel dynamic tuning of a broadband visible metamaterial absorber consisting of a multilayer-graphene-embedded nano-cross elliptical hole (MGENCEH) structure. It has multiple effects, including excitation of surface plasmon polaritons and extraordinary optical transmission in the first two metal layers. A numerical simulation shows that the MGENCEH structure can realize broadband perfect absorption (BPA) from 5.85 × 1014 to 6.5 × 1014 Hz over a wide incident angle range for transverse magnetic polarized light if the chemical potential of graphene (uc) is tuned to 1.0 eV. Furthermore, it has high broadband absorption (above 96%) from 4.6 × 1014 to 6.6 × 1014 Hz and three areas of narrowband perfect absorption around 4.65 × 1014, 5.1 × 1014, and 5.6 × 1014 Hz. The changes in the absorption spectra as a function of uc can be classically explained by simply considering plasmons as damped harmonic oscillators. This BPA is broader than the result of Zhou et al. [Opt. Express 23, A413–A418 (2015)] and is particularly desirable for various potential applications such as solar energy absorbers.

Seeds are essential for the reproduction and dispersion of spermatophytes. The seed life cycle from seed development to seedling establishment proceeds through a series of defined stages regulated by distinctive physiological and biochemical mechanisms. The role of histone modification and chromatin remodeling in seed behavior has been intensively studied in recent years. In this review, we summarize progress in elucidating the regulatory network of these two kinds of epigenetic regulation during the seed life cycle, especially in two model plants, rice and Arabidopsis. Particular emphasis is placed on epigenetic effects on primary tissue formation (e.g., the organized development of embryo and endosperm), pivotal downstream gene expression (e.g., transcription of DOG1 in seed dormancy and repression of seed maturation genes in seed-to-seedling transition), and environmental responses (e.g., seed germination in response to different environmental cues). Future prospects for understanding of intricate interplay of epigenetic pathways and the epigenetic mechanisms in other commercial species are also proposed.

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