With　the　rapidly　increasing　demand　for　security　and　E-health　applications,　device-free　human　detection　has　attracted　interest　because　it　does　not　require　a　wearable　device　or　camera　setup.　This　paper　proposes　a　deep-learning-based　approach　that　monitors　wireless　signals　to　learn　three　human　modes,　i.e.,　absence,　working,　and　sleeping,　in　realistic　indoor　environments.　This　paper　integrates　the　amplitude　and　phase　of　channel　state　information　to　propose　a　hybrid　complex　feature;　this　facilitates　robust　and　efficient　human　detection　even　with　fewer　data　samples.　Experiments　conducted　in　two　unmodified　WiFi　networks　demonstrate　the　effectiveness　of　the　proposed　algorithms.　Four　machine-learning　algorithms　provide　satisfactory　performance　with　sufficient　data,　and　deep　neural　networks　perform　the　best.　Results　show　that　by　using　6%　training　samples,　the　proposed　hybrid　feature　still　achieves　93%　accuracy　and　can　even　outperform　three　typical　machine　learning　algorithms　that　use　full　training　samples.　Moreover,　the　proposed　feature　significantly　improves　detection　accuracy　by　11.62%-27.76%　than　traditional　amplitude　feature　with　fewer　training　samples.　©　1967-2012　IEEE.