# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 3 Proposed Method  
### Overview of Our Method  
In this paper, we used two convolutional neural networks (CNNs) for coarse-to-fine face detection. CNN is actually a multi-layer perception, where each layer is composed of multiple feature maps through different convolutional kernels. The most important advantage of this structure is that the parameters of CNN are self-learned through training data, so the structure avoids the generalization weakness caused by hand-crafted features. CNN usually consists of convolutional layers, pooling layers, and fully connected layers. A convolutional layer is used to extract features, a pooling layer aims to reduce the amount of data for calculation, and a fully connected layer is designed to combine the extracted local features into a powerful global feature. In practical operations, convolution layers and pooling layers are often designed as a whole, i.e., the structure would pool the feature maps after the convolutional operation. However, the output of convolutional layers is not immediately pooled, it is passed to the activation function first and then the results are passed to the pooling layers. Activation functions, which are always nonlinear functions, are used for feature mapping in order to help solve nonlinear problems. The parameters in convolutional layers and pooling layers are learned through the back-propagation algorithm, so the self-learned characteristic makes CNN far superior to the traditional algorithms in precision and robustness in many computer vision tasks such as classification, detection, tracking, etc. Although face detection has received extensive attention in recent years, it still entails many challenges, such as occlusion, complex environments, small targets, etc. This paper aims to solve the problem of tiny-face detection. Due to the low resolution of face images, detecting faces is not an easy task when a pedestrian is far away from the camera. In this situation, the information of a body is much more than the face information. The pedestrians' faces can be roughly calibrated based on body context information. Considering this, we defined the region at the top of the pedestrians' bounding boxes as RoI candidates, obtained the candidate regions by a pedestrian detector and a novel pre-identification algorithm to reduce the search range as pre-processing. Methods of pedestrian detection include the method based on statistical learning methods, background modeling, and neural networks. The most influential pedestrian detection structure is what [PERSON] et al. [48] proposed, which uses Support Vector Machine (SVM) as the classifier to detect faces in the images based on the histograms of oriented gradient (HOG) features. Lots of features have been proposed with better robustness and result in better classification accuracy for pedestrian detection [49; 50; 51; 52]. However, these features are often specifically designed to a particular situation, hence the robustness of these traditional methods is not good. In order to alleviate the problemsmentioned above, more and more researchers have adopted convolutional neural networks (CNNs) to detect objects these years [17; 29; 30; 31; 32; 53]. There are many images from different scenes in the training set, so the robustness of features learned by neural networks is better than hand-crafted features. Therefore, these methods based on CNNs have a huge improvement in accuracy and robustness. Our method consists of two parts, pre-identification detection and face detection. In the pre-identification detection part, we detected the bounding box of the pedestrian by a deep CNN first, then the RoI candidates would be selected by the self-adaptively algorithm, after that we built an image pyramid, which was used to adapt different sizes of faces, as the input for the face detection network. In the face detection part, image pyramid would be passed to our proposed multitask neural network to detect faces.