# Coupling RetinaFace and Depth Information to Filter False Positives
## 1 Introduction
Additionally, the boosting algorithm was effective at reducing false positives, a common issue in detection algorithms. Despite its popularity, the Viola-Jones algorithm had certain limitations. It was not always effective at detecting faces in uncontrolled environments, where it has been known to miss face detections [9]. This is due to the limitations of the Haar-like features utilized by the algorithm, which may struggle to accommodate for variations in lighting and facial expression, or other factors that can influence facial appearance. To overcome these limitations, several extensions and enhancements to the original Haar-like features have been proposed. These include rotated Haar-like features, which are designed to be more robust to rotation, and sparse features, which are designed to be more efficient to compute. In their research, [PERSON] et al. [10] integrated a modified version of the Viola-Jones (VJ) method with an algorithm that can detect salient facial landmarks. Meanwhile, [PERSON] et al. [9] introduced a novel feature named scale-invariant NPD and extended the VJ tree classifier to include a deeper quadratic tree structure. With the advent of deep learning, convolutional neural networks (CNNs) have become increasingly popular for face detection and have shown impressive results in terms of accuracy and speed [11; 12; 13; 14]. These networks are able to learn complex patterns in data and can be trained on large datasets, which has helped improve the performance of face detection algorithms. Additionally, techniques such as transfer learning, which involves using pre-trained CNNs on large datasets and fine-tuning them for specific tasks, have further improved the performance of face detection algorithms. In the context of 2D face detection, deep learning methods have been shown to be effective in detecting faces in images and videos. These methods, such as R-CNN [15] and Deep Dense Face Detector (DDFD) [12], use convolutional neural networks (CNNs) to extract features from images and then classify them using support vector machines (SVMs). These methods have the advantage of being able to handle a wide range of face orientations and sizes without requiring pose or landmark annotations. They have been shown to outperform traditional face detection methods in terms of accuracy and speed. RetinaFace [16] is a face detection algorithm considered to be a state-of-the-art face detector because it is able to combine high-level and low-level semantic information in order to perform single-shot multi-level face localization. This means that it is able to detect faces in a single stage, using a five-level feature pyramid network (FPN) that allows it to process multiscale feature maps. This improves the detection speed of the algorithm, making it faster and more efficient than many other face detection algorithms. The use of 3D information in face detection can improve accuracy by providing additional cues about the shape and structure of the face, which can be used to differentiate it more effectively from other objects in the scene. Depth information can also help resolve occlusions, where part of the face may be obscured by another object, by allowing the algorithm to infer the shape of the face behind the occluder. There are multiple 3D sensors and devices available for face detection, each with its own set of benefits and drawbacks. The Kinect [17] is a popular choice due to its affordability and user-friendliness. However, its performance is limited by the resolution of its depth map and its ability to capture only one depth value per pixel. Advanced sensors such as the MU-2 stereo imaging system [18] and the Minolta Vivid 910 range scanner [19] offer higher-resolution depth maps and more precise depth measurements. Nevertheless, these sensors are usually more expensive and complex to operate. Overall, the use of 3D information in face detection can significantly improve accuracy, but it also introduces additional challenges and complexities in terms of both hardware and software.