現今很多場合中，經常需要計算出席人數作為應用的依據，例如遊樂園入園、博物館入館等，傳統都是使用人力計數器來進行計算，不僅增加了人力成本，對於人數很多的場合，人數計算非常耗時，因此研發有效的人數計算系統也就顯得非常重要。由於深度學習類神經網路的技術成熟，而且分類的效果優良，對於人臉辨認的正確率極高，因此本文提出一個高準確率的自動人數計算系統，透過機器學習方式切割人臉特徵，並且使用卷積神經網路學習人臉五官特徵於確認人臉切割是否正確，達到提高人臉辨認率之目的。首先使用攝像鏡頭拍攝一群人的合照相片，然後以寬鬆條件之Viola-Jones演算法將疑似人臉影像切割成微影像，接著透過卷積神經網路確認切割人臉的正確性，並且排除非人臉的切割範圍，達到提升人臉辨認與人數計算的正確率目的，實驗結果以精確率(precision rate)、召回率(recall rate)、F測度(F-measure)檢測人數計算的效能，也證實本文提出方法確實可快速而有效計算人數與標示人臉位置。
Calculation of the number of people is often required in life, such as repeating counting the number of tourists in a tour group, in a play area etc. If there are a lot of people, the calculation of their number is time consuming. Developing an efficient method to calculate the number of people is really important. Due to recent advances in face detection technologies, faces in a photo can be used as a key for the calculation of the number of people in the scene. In this paper, we propose a simple and effective approach to calculating the number of people in a photo based on a convolutional neural network (CNN) for face recognition. Firstly, the suspected face areas are segmented into blocks by the Viola-Jones algorithm. Then, the detected face blocks are refined through the CNN by excluding the segmented blocks without the human face, so as to enhance the accuracy of face detection. The experimental results show that the proposed system can efficiently and correctly calculate the number of people in a photo, where the performances are presented by the precision rate, recall rate, and F-measure.