This　paper　presents　a　method　of　depression　recognition　based　on　direct　measurement　of　affective　disorder.　Firstly,　visual　emotional　stimuli　are　used　to　obtain　eye　movement　behavior　signals　and　physiological　signals　directly　related　to　mood.　Then,　in　order　to　eliminate　noise　and　redundant　information　and　obtain　better　classification　features,　statistical　methods　(FDR　corrected　t-test)　and　principal　component　analysis　(PCA)　are　used　to　select　features　of　eye　movement　behavior　and　physiological　signals.　Finally,　based　on　feature　extraction,　we　use　kernel　extreme　learning　machine　(KELM)　to　recognize　depression　based　on　PCA　features.　The　results　show　that,　on　the　one　hand,　the　classification　performance　based　on　the　fusion　features　of　eye　movement　behavior　and　physiological　signals　is　better　than　using　a　single　behavior　feature　and　a　single　physiological　feature;　on　the　other　hand,　compared　with　previous　methods,　the　proposed　method　for　depression　recognition　achieves　better　classification　results.　This　study　is　of　great　value　for　the　establishment　of　an　automatic　depression　diagnosis　system　for　clinical　use.