# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 2 Related Works  
Face detection is the key step in many different face-related applications and studies [19]. Most of the early work was designed for high-resolution images and large targets by using statistical learning methods to automatically extract features. [PERSON] et al. [20] proposed a framework to detect faces by utilizing histogram-based feature extraction with random subspace and voting ensemble learners. [PERSON] et al. [21] proposed a face location algorithm which is developed to extract face regions with a high proportion of skin. This type of method increases the speed of the operation. Since the features are not completely selected by humans, the robustness is improved. However, the effect is still poor when the target is small. [PERSON] et al. [22] proposed a new feature and combined it with the gray level feature and skin color feature, and this method improves detection speed in complex backgrounds and reduces the computational complexity. Additionally, [PERSON] et al. [23] used the geometric relationship between facial organs to generate four Haar-like features for face detection based on the traditional Adaboost classifier, which greatly reduces the detection time. One of the most impressive traditional methods is Viola-Jones [24] which designed cascade classifiers based on the Haar feature and AdaBoost classifier, but all of these traditional methods focus on improving the performance with more effective hand-crafted features and more powerful classifiers [25; 26; 27; 28]. These features or detection structures have certain subjective factors, which leads to the robustness of these frameworks being poor, and the operation being complicated and time consuming. In recent years, deep learning models offer new ideas in solving many research problems such as classification, object detection, image segmentation, image restoration etc. Convolutional neural networks have achieved remarkable success in many different tasks of computer vision, especially for object detection. The family of Region-based Convolutional Neural Networks (R-CNN) [17; 29; 30], You Only Look Once (YOLO) [31], and Single Shot Multibox Detector (SSD) [32] are the most efficient and popular approaches for detecting objects these years. Inspired by these brilliant methods, researchers have proposed many effective and robust face detection structures. [PERSON] et al. [33] investigated applying faster R-CNN to face detection, compared with previous proposed models, and its accuracy has a significant improvement. Contextual multi-scale region-based CNN (CMS-RCNN) [34] combined multi-scale information which consists of the region proposal component and the RoI detection component to detect faces. [PERSON] et al. [35] improved Faster R-CNN with the hard negative mining and significant boosts, and the hard negatives harvested from a large set of background examples. [PERSON] et al. [36] applied some effective strategies including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration to improve Faster R-CNN in face detection. [PERSON] et al. [37] light-designed R-CNN and improved the performance by integrating multi-scale training, multi-scale testing, some tricks for inference, and a vote-based ensemble method. Enlightened by SSD, [PERSON] et al. [38] proposed Multiple Dropout Framework to detect faces. Due to the anchor mechanism, the family of R-CNN can detect objects which occupy the majority of an image which are clear and huge, but the accuracy of these methods drop rapidly when the target is small. [PERSON] et al. [39] proposed a novel cross-level parallel network (CLPNet) to extract low-level features and fuse them in the high-level stage, and CLPNet achieved remarkable performance on crowd counting. [PERSON] et al. [40] proposed a novel lightweight deep neural network and a new training method of progressive positive and hard negative sample mining to improve training speed and accuracy.