This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints). There is large amount of prior work on each of these tasks separately, yet, to the best of our knowledge, this is the first time in literature that the interplay between pose estimation and semantic part segmentation is investigated. To address this limitation of prior work, in this paper, we propose a CNN cascade of tasks that firstly performs landmark localisation and then uses this information as input for guiding semantic part segmentation. We applied our architecture to the problem of facial part segmentation and report large performance improvement over the standard unguided network on the most challenging face datasets. Testing code and models will be published online at this http URL.
One of the first face segmentation methods prior to deep learning is known as LabelFaces @cite_23 which is based on patch classification and further refinement via a hierarchical face model. Another hierarchical approach to face segmentation based on Restricted Boltzmann Machines was proposed in @cite_3 . More recently, a multi-objective CNN has been shown to perform well for the task of face segmentation in @cite_27 . The method is based on a CRF the unary and pairwise potentials of which are learnt via a CNN. Softmax loss is used for the segmentation masks, and a logistic loss is used to learn the edges. Additionally, the network makes use of a non-parametric segmentation prior which is obtained as follows: first facial landmarks on the test image are detected and then all training images with most similar shapes are used to calculate an average segmentation mask. This mask is finally used to augment RGB. This segmentation mask might be blurry, does not encode pose information and results in little performance improvement.
@cite_27 @cite_3 @cite_23
1905033729 1980163762 2151971489
This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies. Moreover, we regularize the network by using a nonparametric prior as new input channels in addition to the RGB image, and show that significant performance improvements can be achieved with a much smaller network size. Experiments on both the LFW and Helen datasets demonstrate state-of-the-art results of the proposed algorithm, and accurate labeling results on challenging images can be obtained by the proposed algorithm for real-world applications. This paper investigates how to parse (segment) facial components from face images which may be partially occluded. We propose a novel face parser, which recasts segmentation of face components as a cross-modality data transformation problem, i.e., transforming an image patch to a label map. Specifically, a face is represented hierarchically by parts, components, and pixel-wise labels. With this representation, our approach first detects faces at both the part- and component-levels, and then computes the pixel-wise label maps (Fig.1). Our part-based and component-based detectors are generatively trained with the deep belief network (DBN), and are discriminatively tuned by logistic regression. The segmentators transform the detected face components to label maps, which are obtained by learning a highly nonlinear mapping with the deep autoencoder. The proposed hierarchical face parsing is not only robust to partial occlusions but also provide richer information for face analysis and face synthesis compared with face keypoint detection and face alignment. The effectiveness of our algorithm is shown through several tasks on 2, 239 images selected from three datasets (e.g., LFW [12], BioID [13] and CUFSF [29]). We consider the problem of parsing facial features from an image labeling perspective. We learn a per-pixel unary classifier, and a prior over expected label configurations, allowing us to estimate a dense labeling of facial images by part (e.g. hair, mouth, moustache, hat). This approach deals naturally with large variations in shape and appearance characteristic of unconstrained facial images, and also the problem of detecting classes that may be present or absent. We use an Adaboost-based unary classifier, and develop a family of priors based on ‘epitomes’ which are shown to be particularly effective in capturing the non-stationary aspects of face label distributions.