# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 4 Experiments  
The experimental environment used in this paper is as follows: Intel Xeon E-2136 CPU @3.30 GHz, 16 GB internal storage, Windows 10 64 bit operating system, Microsoft, US. We tested the performance of our framework on different datasets composed of a self-collected dataset and Caltech Pedestrian dataset 18. The self-collected dataset was introduced in Section 1. The Caltech dataset was collected by the California Institute of Technology on 2012, and it is often used in the design and testing of pedestrian detection algorithms. It contains a video of a city environment with a duration of about 10 h, and the image resolution is \(640\times 480\). The pedestrian targets are divided into different levels according to the size and the occlusion. Figure 2 shows PMCD could detect the face accurately when the pedestrians are in different environments containing occlusion, incomplete body, and poor light. Our mechanism consists of SSD [32] and the pre-identification mechanism, which is mentioned in Section 3.2.2. The accuracy of face detection in our framework is directly related to the performance of the mechanism, so we tested our mechanism on the Caltech Pedestrian dataset. In order to prove that our mechanism is superior to other state-of-the-art methods, we drew the curve of false positive per image (FPPI)-miss rate by the evaluation method [54], as shown by:  
\[\text{FPPI}=\text{m}/\text{N}, \tag{7}\]  
where m is the number of false positive, N is the number of images. \[\text{miss rate}=\text{fa}/(\text{fa}+\text{tr}), \tag{8}\]  
where fa is the number of false negatives, tr is the number of true positives. We compared our pre-identification mechanism with Viola-Jones (VJ) [24], histogram of oriented gradient (HOG) [48], Scale Aware (SA)-Fast RCNN [55] and region proposal network(RPN)+ boosted forests (BF) [56]. As shown in Figure 3, ours performs better than the other methods, and the detection results of the corresponding method are better when the miss rate is lower. As shown in Figure 3, SSD achieves the best performance in these methods. Meanwhile, the miss rates of VJ and HOG are high, the main reason is that traditional methods usually train the classifiers by hand-crafted features, resulting in poorer robustness than that of convolutional neural networks. Figure 3: The curve of false positive per image (FPPI)-miss rate. Figure 2: Face detection in different situations based on pre-identification mechanism and a cascaded detector (PMCD): (**a**) the face detected with occlusion on some part of the body; (**b**) the face detected with occlusion on some part of the body in complex backgrounds; (**c**) the tiny face detected with good light; (**d**) the tiny face detected with poor light. We compared the whole framework with five of the most important face detection algorithms on the self-collected dataset, which are Viola-Jones(VJ) [24], single shot multibox detector(SSD) [32], multi-task cascaded neural networks (MTCNN) [45], multi-channel shallow convolutional network(MMSC) [46], and hybrid resolutions(HR) [47]. Figure 4 shows the results of the six methods in different situations. VJ and MTCNN cannot detect faces correctly when the face is fairly small. MMSC uses traditional methods to extract pre-processing regions, and thus it cannot keep good performance in detecting faces on incomplete bodies or in relatively complex background. SSD can detect faces roughly, but it cannot correctly obtain the bounding boxes when the faces are small. On one hand, HR can achieve remarkable performance in detecting tiny faces when the environment is relatively simple, but when the light is dim or faces are in a complex environment, the performance of HR would be very bad. On the other hand, PMCD achieves the best performance among the six methods. In the case of poor lighting conditions, only PMCD can achieve remarkable performance. Intersection over union (IoU) is the ratio between the intersection and the union of the candidate bounding box predicted by the model and the ground truth bounding box.