# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 1 Introduction  
Face detection is one of the most hot topics in computer vision as it is a key step for many different applications, such as face recognition [1], facial expression analysis [2], eye-tracking [3], facial performance capture [4], facial expression transformation [5], etc. In fact, the applications are not limited to the traditional areas, there are still some exciting interdisciplinary applications [6; 7; 8; 9; 10; 11] in the field of animation. However, many factors such as the illumination, occlusion, and the diversity of faces cause huge challenges in face detection. Using universal face templates to detect faces is one of the main research fields of traditional methods. Determining whether there is a face is undertaken by calculating the correlation coefficient between the area which is detected and the template [12]. However, facial skin color, different expressions, and occlusion lead to the method being less robust and computationally complex. In recent years, many researchers shift their attention from the traditional methods to convolutional neural networks (CNNs) [13; 14; 15] since they have achieved remarkable success in many important tasks of computer vision, such as classification, detection, and recognition. Lots of approaches have been proposed to solve the problem of tiny-face detection, which aims to search a tiny face in a whole image,especially in a low-resolution image. However, these methods cannot achieve satisfactory performance because the features of tiny faces are different from those of big faces and tiny faces contain limited information available for face detection. It is challenging to detect small objects in image detection. That is because these networks are designed to propose default boxes and the classification score is calculated from one single deep CNN. For example, faster Region-based Convolutional Neural Networks (R-CNN) [16] extracts features by visual geometry group (VGG)-16 [17], but when the face size is less than \(16\,\times\,16\), the output in 'conv5' is less than one pixel. As the convolutional layer is deeper, each pixel in the feature map gathers more information outside of the original input area and lower information of the region of interest (RoI), which means these methods cannot keep the performance when targets are small. However, a single shallow CNN cannot get enough information for object detection, and thus a cascaded structure which is usually divided into a prediction part and a regression part becomes a popular and effective framework for face detection. A cascade framework can generate a large number of bounding box candidates based on a low threshold, and then extracts the regression scores of these candidates. That is, the information loss of shallow convolutional layers can be effectively relieved. Moreover, the above-mentioned face detection algorithms cannot keep good performance in video surveillance, because they are designed for high-resolution images and big faces. Limited by the cost of large-scale surveillance and the scale of data, the face targets are often small and not clear. Additionally, the head movement tends to be more frequent than the body, making it difficult to detect tiny faces in multiple views. Previous methods are difficult to accurately detect tiny faces in this kind of scenario. If tiny faces can be detected automatically in real time, the telephoto lens would immediately be aimed at the suspicious persons' faces, which can help the police to conduct reconnaissance. This is very useful in public security, since violent incidents and terrorist attacks have occurred in many places in recent years. In order to alleviate the above problems, this paper presents a cascaded framework named pre-identification mechanism and a cascaded detector (PMCD) to detect tiny faces based on two independent CNNs and a pre-identification mechanism. The two sub-networks can be trained separately, which greatly improves the flexibility in training networks.