Face recognition-based IMDB plug-in for movies

In this paper, we present an initial study on an IMDB plug-in for cast identification in movies. In the system, training face images are collected by using Google image search. While watching a movie, the user clicks on the face of the person he is interested to acquire information. Afterwards, the system first tries to detect close to frontal faces, if it cannot find any, then it runs a profile face detector. The found face are then tracked backwards and forwards in the shot and this way a face sequence is obtained. Matching is performed between the extracted face sequence from the movie and the face image sets collected from the web. IMDB page links of the closest three persons resulted from the matching process is then presented to the user. In this study, we addressed the following three interesting points: matching between face sequence and face image sets, the effect of automatically collected noisy training images from the web on the performance, and finally, the performance effect of utilizing prior information of cast list and performing the classification within a limited number of classes. Experiments have shown that matching between face sequence and face image sets is a difficult problem.