Despite　gait　recognition　and　person　re-identification　researches　have　made　a　lot　of　progress,　the　accuracy　of　identification　is　not　high　enough　in　some　specific　situations,　for　example,　people　carrying　bags　or　changing　coats.　In　order　to　alleviate　above　situations,　we　propose　a　simple　but　effective　Consecutive　Horizontal　Dropout　(CHD)　method　apply　on　human　feature　extraction　in　deep　learning　network　to　avoid　overfitting.　Within　the　CHD,　we　intensify　the　robust　of　deep　learning　network　for　cross-view　gait　recognition　and　person　re-identification.　The　experiments　illustrate　that　the　rank-1　accuracy　on　cross-view　gait　recognition　task　has　been　increased　about　10%　from　68.0%　to　78.201%　and　8%　from　83.545%　to　91.364%　in　person　reidentification　task　in　wearing　coat　or　jacket　condition.　In　addition,　100%　accuracy　of　NM　condition　was　first　obtained　with　CHD.　On　the　benchmarks　of　CASIA-B,　above　accuracies　are　state-of-thearts.　©　2019　Association　for　Computing　Machinery.