Landmark localization in images and videos is a classic problem solved in various ways. Nowadays, with deep networks prevailing throughout machine learning, there are revamped interests in pushing facial landmark detection technologies to handle more challenging data. Most efforts use network objectives based on L1 or L2 norms, which have several disadvantages. First of all, the locations of landmarks are determined from generated heatmaps (i.e., confidence maps) from which predicted landmark locations (i.e., the means) get penalized without accounting for the spread: a high scatter corresponds to low confidence and vice-versa. For this, we introduce a LaplaceKL objective that penalizes for a low confidence. Another issue is a dependency on labeled data, which are expensive to obtain and susceptible to error. To address both issues we propose an adversarial training framework that leverages unlabeled data to improve model performance. Our method claims state-of-the-art on all of the 300W benchmarks and ranks second-to-best on the Annotated Facial Landmarks in the Wild (AFLW) dataset. Furthermore, our model is robust with a reduced size: 1 8 the number of channels (i.e., 0.0398MB) is comparable to state-of-that-art in real-time on CPU. Thus, we show that our method is of high practical value to real-life application.
has been of interest to researchers for decades. At first, most methods were based on Active Shape Models @cite_4 and Active Appearance Models @cite_25 . Then, Cascaded Regression Methods (CRMs) were introduced, which operate in a sequential fashion; starting with the average shape, then incrementally shifting the shape closer to the target shape. CRMs offer high speed and accuracy ( @math 1,000 fps on CPU @cite_11 @cite_13 ).
@cite_13 @cite_4 @cite_25 @cite_11
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The “interpretation through synthesis”, i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at “explaining” face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial poses, occlusions, lighting, low resolution, etc. Since these variations are mostly non-linear, it is impossible to represent them in a linear model, such as Principal Component Analysis (PCA). This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations. In this approach, three crucial components represented in hierarchical layers are modeled using the Deep Boltzmann Machines (DBM) to robustly capture the variations of facial shapes and appearances. DAMs are therefore superior to AAMs in inferring a representation for new face images under various challenging conditions. In addition, DAMs have ability to generate a compact set of parameters in higher level representation that can be used for classification, e.g. face recognition and facial age estimation. The proposed approach is evaluated in facial image reconstruction, facial super-resolution on two databases, i.e. LFPW and Helen. It is also evaluated on FG-NET database for the problem of age estimation. In this letter, we present a random cascaded-regression copse (R-CR-C) for robust facial landmark detection. Its key innovations include a new parallel cascade structure design, and an adaptive scheme for scale-invariant shape update and local feature extraction. Evaluation on two challenging benchmarks shows the superiority of the proposed algorithm to state-of-the-art methods. © 1994-2012 IEEE. In this paper, we present the details of our method in attending the 300 Faces in-the-wild (300W) challenge. We build our method on cascade regression framework, where a series of regressors are utilized to progressively refine the shape initialized by face detector. In cascade regression, we use the HOG feature in a multi-scale manner, where the large pose validation is handled in early stages by HOG feature at large scale, and then shape is refined at later stages with HOG feature at small scale. We observe that the performance of the cascade regression method decreases when the initialization provided by face detector is not accurate enough (for faces with large appearance variations, face detection is still a challenging problem). To handle the problem, we propose to generate multiple hypotheses, and then learn to rank or combine these hypotheses to get the final result. The parameters in both learn to rank and learn to combine can be learned in a structural SVM framework. Despite the simplicity of our method, it achieves state-of-the-art performance on LFPW, and dramatically outperforms the baseline AAM on the 300-W challenge. Mainstream direction in face alignment is now dominated by cascaded regression methods. These methods start from an image with an initial shape and build a set of shape increments by computing features with respect to the current shape estimate. These shape increments move the initial shape to the desired location. Despite the advantages of the cascaded methods, they all share two major limitations: (i) shape increments are learned separately from each other in a cascaded manner, (ii) the use of standard generic computer vision features such SIFT, HOG, does not allow these methods to learn problem-specific features. In this work, we propose a novel Recurrent Convolutional Face Alignment method that overcomes these limitations. We frame the standard cascaded alignment problem as a recurrent process and learn all shape increments jointly, by using a recurrent neural network with the gated recurrent unit. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features, widely adopted in the literature and thus allowing the model to learn task-specific features. Moreover, both the convolutional and the recurrent neural networks are learned jointly. Experimental evaluation shows that the proposed method has better performance than the state-of-the-art methods, and further support the importance of learning a single end-to-end model for face alignment.