Face detection has achieved great success using the region-based methods. In this report, we propose a region-based face detector applying deep networks in a fully convolutional fashion, named Face R-FCN. Based on Region-based Fully Convolutional Networks (R-FCN), our face detector is more accurate and computational efficient compared with the previous R-CNN based face detectors. In our approach, we adopt the fully convolutional Residual Network (ResNet) as the backbone network. Particularly, We exploit several new techniques including position-sensitive average pooling, multi-scale training and testing and on-line hard example mining strategy to improve the detection accuracy. Over two most popular and challenging face detection benchmarks, FDDB and WIDER FACE, Face R-FCN achieves superior performance over state-of-the-arts.
In the past decades, face detection has been extensively studied. The pioneering work of Viola and Jones @cite_3 invents a cascaded AdaBoost face detector using Haar-like features. proposed to learn the cascaded AdaBoost classifier based on Haar-like features for face detection. After that, numerous of works @cite_28 @cite_33 @cite_21 have focused on developing more advanced features and more powerful classifiers. Besides the boosted cascade methods, several studies apply deformable part models (DPM) for face detection @cite_30 @cite_14 @cite_4 . Besides the cascade based face detection methods, @cite_30 @cite_14 @cite_4 use the deformable part models (DPM) for face detection. The DPM methods detect faces by modeling the relationship of deformable facial parts. define a face with a collection of deformable parts and train a classifier to find these parts and their relationship.
@cite_30 @cite_14 @cite_4 @cite_33 @cite_28 @cite_21 @cite_3
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A cascade face detector uses a sequence of node classifiers to distinguish faces from nonfaces. This paper presents a new approach to design node classifiers in the cascade detector. Previous methods used machine learning algorithms that simultaneously select features and form ensemble classifiers. We argue that if these two parts are decoupled, we have the freedom to design a classifier that explicitly addresses the difficulties caused by the asymmetric learning goal. There are three contributions in this paper: The first is a categorization of asymmetries in the learning goal and why they make face detection hard. The second is the forward feature selection (FFS) algorithm and a fast precomputing strategy for AdaBoost. FFS and the fast AdaBoost can reduce the training time by approximately 100 and 50 times, in comparison to a naive implementation of the AdaBoost feature selection method. The last contribution is a linear asymmetric classifier (LAC), a classifier that explicitly handles the asymmetric learning goal as a well-defined constrained optimization problem. We demonstrated experimentally that LAC results in an improved ensemble classifier performance. We present a novel boosting cascade based face detection framework using SURF features. The framework is derived from the well-known Viola-Jones (VJ) framework but distinguished by two key contributions. First, the proposed framework deals with only several hundreds of multidimensional local SURF patches instead of hundreds of thousands of single dimensional haar features in the VJ framework. Second, it takes AUC as a single criterion for the convergence test of each cascade stage rather than the two conflicting criteria (false-positive-rate and detection-rate) in the VJ framework. These modifications yield much faster training convergence and much fewer stages in the final cascade. We made experiments on training face detector from large scale database. Results shows that the proposed method is able to train face detectors within one hour through scanning billions of negative samples on current personal computers. Furthermore, the built detector is comparable to the state-of-the-art algorithm not only on the accuracy but also on the processing speed. We present a face detection algorithm based on Deformable Part Models and deep pyramidal features. The proposed method called DP2MFD is able to detect faces of various sizes and poses in unconstrained conditions. It reduces the gap in training and testing of DPM on deep features by adding a normalization layer to the deep convolutional neural network (CNN). Extensive experiments on four publicly available unconstrained face detection datasets show that our method is able to capture the meaningful structure of faces and performs significantly better than many competitive face detection algorithms. Face detection using part based model becomes a new trend in Computer Vision. Following this trend, we propose an extension of Deformable Part Models to detect faces which increases not only precision but also speed compared with current versions of DPM. First, to reduce computation cost, we create a lookup table instead of repeatedly calculating scores in each processing step by approximating inner product between HOG features and weight vectors. Furthermore, early cascading method is also introduced to boost up speed. Second, we propose new integrated model for face representation and its score of detection. Besides, the intuitive non-maximum suppression is also proposed to get more accuracy in detecting result. We evaluate the merit of our method on the public dataset Face Detection Data Set and Benchmark (FDDB). Experimental results shows that our proposed method can significantly boost 5.5 times in speed of DPM method for face detection while achieve up to 94.64 the accuracy of the state-of-the-art technique. This leads to a promising way to combine DPM with other techniques to solve difficulties of face detection in the wild.