Visual people counting using gender features and LRU updating scheme

The general public spends a significant amount of time in front of digital signage seeking information from many venues such as exhibition halls and shopping centers. This is why advertisement purchasers believe that the number of passing viewers provides crucial information for their marketing strategies. In this paper, a real-time person counting/memorizing system is designed capable of distinguishing the gender of potential customers. An adaptive boosting (Adaboost) machine learning algorithm is used to detect human faces and utilize specific filtering criteria to eliminate useless data. For each detected person, face and torso information are recorded in a database for identification. The least recently used identification record will be deleted if the database is full. Gender classification is performed by support vector machine using hair ratios extracted from gender characterizing regions. Based on a variety of experiments, the accuracy of the proposed algorithm is higher than 90 % for person-counting and higher than 94 % for gender classification. Moreover, the execution speed on personal computers may reach 15–20 fps.