# An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
## 1 Introduction  
Face detection is one of the most classic problems in computer vision. It can be widely used in many areas, such as face recognition [1], people counting [2; 3], eye movement tracking [4], etc. Many approaches for face detection have been proposed. Some approaches use channel feature-based methods [5; 6] that are able to encode each image channel as rich information in a simple form such as gradient magnitude and oriented gradient histograms. Some approaches consider faces as a combination of facial parts and convert the face detection problem as the part detection problem [7; 8].  
Some other approaches use a cascade of classifiers for face detection. In many circumstances, a cascade framework is preferable for face detection because it can utilize a set of weak classifiers to improve accuracy, and it can sometimes reject negative samples in the early stage to improve efficiency. For example, the Viola-Jones face detector adopts simple Haar-like features, allowing fast evaluation, and it uses boosted cascade to construct an ensemble of the simple features to achieve an accurate face detector [9]. After that, a cascade structure became a popular and effective framework for practical face detection. Many improvements have been made based on the Viola-Jones face detector during the past few years, and most of them adopt more complex features to improve the detection performance [10; 11; 12].  
However, if we apply the aforementioned face detection algorithms to images captured from surveillance cameras for large and crowded places, these algorithms could have very poor performance. This is because these face detection algorithms are designed for relatively large faces in high resolution images, which is not possible for large area surveillance due to hardware and bandwidth costs. For large scene surveillance, surveillance cameras are generally installed at relatively high positions in order to obtain a large field of view, and thus far from the faces. Therefore, the captured faces are mostly at a small scale. In addition, the facial details in this situation are not clear and are heavily affected by background and illumination.  
Therefore, in this work, we focus on improving neural network cascade for large scene surveillance and tackle it by designing a novel multi-scale cascade that is able to exploit head-shoulder information. It can be useful in many applications. For example, recently, public safety has attracted more and more attention. Many violent incidents and terrorist attacks happened in a number of countries. Among those, a large amount of deaths and injuries mostly occurred in large and crowded places, such as squares, stations, stadiums, theaters, etc. If we could accurately detect faces for individuals in these areas, we would be able to direct high resolution face recognition cameras with telephoto lenses to take pictures of each face of interest exactly. This can help us identify suspicious criminals or terrorists in advance, and consequently improve public safety. Face detection for large scene surveillance can also help in other tasks, for example estimating people densities.