IMAGE-SET FACE RECOGNITION BASED ON TRANSDUCTIVE LEARNING

In this paper we consider the problem of face recognition in a scenario when the query consists of a set of images and the gallery contains a single still image per subject. This is a more challenging problem compared to image-set to image-set matching and has wider applications in advanced surveillance, smart access control and human-computer interaction. Unfortunately most of the previous matching strategies in literature fail to work or deteriorate drastically if they are provided with one sample per class as the gallery data. In this paper we demonstrate how transductive learning can be utilized to map the image-set to single image matching problem into the recently-studied framework of set matching using canonical correlations. Experimental results on different challenging datasets reveal the efficiency of the proposed method against existing approaches.