## 1 Introduction
[8] proposed deep cascaded multi-task convolutional neural networks that exploited the inherent correlation between detection and alignment to boost face detection performance. An online hard sample mining strategy was also applied. [PERSON] et al. [9] combined the CNN, a blur-aware bi-channel network, and a self-learning mechanism for exploiting video contexts continuously and face detection. [PERSON] et al. [11] used a CNN for face detection by scoring facial parts responses according to the spatial structure and arrangement. The scoring mechanism is data-driven. Their method can detect faces under severe occlusion and unconstrained pose variations. [PERSON] et al. [13] proposed an algorithm for simultaneous face detection, landmarks localization, pose estimation, and gender recognition using CNN. They fused the intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the mixed features, enabling the performance to be boosted. [PERSON] et al. [17] proposed using canonical correlation analysis to fuse the local face pattern extracted by the AlexNet and a shallow CNN. A multi-support vector machine then classifies the emotion category, where the accuracy rate can reach 87.69%. Based on the above discussions, the methods in [8; 13; 17; 24] use a deep cascaded multi-task framework and complex network for multi-function target recognition. The computational duty is high, while the size of the trained net is large. Loading the net for recognition is time-consuming. In this study, we attempt to develop a light app for quickly calculating the number of people, while loading the trained net requires little time. Therefore, the app is lightweight and practical. The VJ algorithm can effectively segment faces using machine learning in a photo. Additionally, CNN can effectively recognize the faces in a photo. In this paper, we propose an automatic and simple app to calculate the number of people in a photo by integrating the VJ algorithm and a face CNN. Initially, the VJ algorithm is employed to segment the faces as blocks in a photo. In this stage, all face-like blocks should be segmented out by the VJ algorithm, no matter how high the false detection rate is, so the miss detection of the face blocks is shallow. These segmented blocks are utilized for training a CNN for face confirmation. The face blocks detected by the VJalgorithm are refined by the face CNN, enabling the segmented blocks without the human face to be excluded; meanwhile, the actual face blocks are preserved. Accordingly, the accuracy of the detected face blocks is much increased. Finally, the number of detected face blocks is summed up to obtain the number of humans in a photo. The experimental results reveal that the proposed method can effectively calculate the number of people in a photo with the average recall rate exceeding 100% in most conditions. It should be mentioned that this work aims to propose a practical app for quick calculating the number of people in a photo rather than develop an approach for face recognition. The proposed app is practical and can be applied in real environments. In response to the current expansion of the COVID-19 epidemic, if there are tour groups, out-of-school teaching activities, etc., the proposed app can help quickly calculate the number of people, avoid crowd gathering, and cause the risk of group infections. In particular, the help in calculating a large number of people is significant in real environments. The rest of this paper is organized as follows: Section 2 describes the proposed app for automatically calculating people's numbers. Section 3 shows the experimental results. Finally, Section 4 concludes this study.