# Coupling RetinaFace and Depth Information to Filter False Positives
## 1 Introduction  
Face detection algorithms have been developed to automate the process of identifying and locating faces in digital images, driven by the growing demand for such capabilities [1, 2, 3]. These algorithms have undergone a series of advancements, beginning with knowledge-based methods that relied on human expertise to define the features of a face, followed by feature-invariant approaches that aimed to recognize faces based on their geometrical properties, such as the relative positioning of the eyes, nose, and mouth. In response to the challenge of face detection, researchers have developed a variety of techniques utilizing both traditional computer vision methods and more contemporary deep learning methods [4, 5]. Traditional methods frequently rely on handcrafted features and ensembles of classifiers to detect faces in images. Although computationally efficient, these methods often encounter difficulties with variations in facial appearance and are sensitive to changes in illumination and pose. More recent approaches based on deep learning have shown great promise in overcoming these limitations. These methods use convolutional neural networks (CNNs) trained on large datasets of face images to learn highly discriminative features for face detection. CNNs have the advantage of being able to automatically learn features from data, which can be more robust and generalizable than hand-crafted features. Additionally, CNNs can be trained using large-scale parallel computing, which allows for efficient training of very deep and complex models. The single-shot detector (SSD) is one of the most renowned face detection systems that employs deep learning [6]. It utilizes a convolutional neural network (CNN) to anticipate bounding boxes and class probabilities for faces in an image. SSDs are celebrated for their ability to deliver high-speed and real-time performance, rendering them ideal for use in applications such as video surveillance and face tracking. Another popular approach to face detection is the multi-task cascade convolutional neural network (MTCNN) [7], which uses a cascade of three CNNs to first identify potential face regions, refine the bounding boxes and facial landmarks, and finally classify the detected faces. MTCNNs have been shown to achieve high accuracy on a range of face detection benchmarks. Overall, face detection remains a challenging problem due to the wide variations in face appearance and the need for real-time performance. However, the development of deep learning methods has greatly improved the state of the art in face detection and opened up new possibilities for applications that rely on the detection and analysis of faces. In contrast, template matching methods [5] employ a pre-established template of a face to look for corresponding matches in an image. While these methods can be successful, they have certain limitations. Specifically, the template must be meticulously constructed to accommodate for variations in facial appearance, thereby restricting the efficacy of this approach. Appearance-based methods, also known as holistic methods, have become increasingly popular in recent years. These methods use machine learning techniques to learn the characteristic features of a face from a large dataset of labeled images. Because these methods can learn to recognize faces automatically, they are not limited by the constraints of template matching approaches. The Viola-Jones algorithm (VJ) [8] was a popular and effective method for detecting objects in images. It was specifically designed for object detection and relied on three techniques: an integral image strategy for the efficient extraction of Haar features, an ensemble called AdaBoost, and an attentional cascade structure. One of the main advantages of the Viola-Jones algorithm was its efficiency, which allowed it to run in real-time on standard hardware. This was achieved through Haar-like features, i.e., simple rectangular shapes that can be calculated quickly and easily.