Face　anti-spoofing　(FAS)　is　important　to　securing　face　recognition.　Most　of　the　existing　methods　regard　FAS　as　a　binary　classification　problem　between　bona　fide　(real)　and　spoof　images,　training　their　models　from　only　the　perspective　of　Real　vs.　Spoof.　It　is　not　beneficial　for　a　comprehensive　description　of　real　samples　and　leads　to　degraded　performance　after　extending　attack　types.　In　fact,　the　spoofing　clues　in　various　attacks　can　be　significantly　different.　Furthermore,　some　attacks　have　characteristics　similar　to　the　real　faces　but　different　from　other　attacks.　For　example,　both　real　faces　and　video　attacks　have　dynamic　features,　and　both　mask　attacks　and　real　faces　have　depth　features.　In　this　paper,　a　Multi-Perspective　Feature　Learning　Network　(MPFLN)　is　proposed　to　extract　representative　features　from　the　perspectives　of　Real　+　Mask　vs.　Photo　+　Video　and　Real　+　Video　vs.　Photo　+　Mask.　And　using　these　features,　a　binary　classification　network　is　designed　to　perform　FAS.　Experimental　results　show　that　the　proposed　method　can　effectively　alleviate　the　above　issue　of　the　decline　in　the　discrimination　of　extracted　features　and　achieve　comparable　performance　with　state-of-the-art　methods.