Human　faces　are　almost　always　the　focus　of　visual　attention　because　of　the　rich　semantic　information　therein.　While　some　visual　attention　models　incorporating　face　cues　indeed　perform　better　in　images　with　faces,　yet　there　is　no　systematic　analysis　of　the　deployment　of　visual　attention　on　human　faces　in　the　context　of　visual　attention　modelling,　nor　is　there　any　specific　attention　model　designed　for　face　images.　On　faces,　many　high-level　factors　have　influence　on　visual　attention.　To　investigate　visual　attention　on　human　faces,　we　first　construct　a　Visual　Attention　database　for　Faces　(VAF　database),　which　is　composed　of　481　face　images　along　with　eye-tracking　data　of　22　viewers.　Statistics　of　the　eye　movement　data　show　that　some　high-level　factors　such　as　face　size,　facial　features　and　face　pose　have　impact　on　visual　attention.　Thus　we　propose　to　build　visual　attention　models　specifically　for　face　images　through　combining　low-level　saliency　calculated　by　traditional　saliency　models　with　high-level　facial　features.　Efficiency　of　the　built　models　is　verified　on　the　VAF　database.　When　combined　with　high-level　facial　features,　most　saliency　models　can　achieve　better　performance.　(C)　2017　Elsevier　Inc.　All　rights　reserved.