In　this　paper,　we　propose　a　novel　view-invariant　gait　authentication　method　based　on　silhouette　contours　analysis　and　view　estimation.　The　approach　extracts　Lucas-Kanade　based　gait　flow　image　and　head　and　shoulder　mean　shape　(LKGFI-HSMS)　of　a　human　by　using　the　Lucas-Kanade0s　method　and　procrustes　shape　analysis　(PSA).　LKGFI-HSMS　can　preserve　the　dynamic　and　static　features　of　a　gait　sequence.　The　view　between　a　person　and　a　camera　is　identified　for　selecting　the　target＇s　gait　feature　to　overcome　view　variations.　The　similarity　scores　of　LKGFI　and　HSMS　are　calculated.　The　product　rule　combines　the　two　similarity　scores　to　further　improve　the　discrimination　power　of　extracted　features.　Experimental　results　demonstrate　that　the　proposed　approach　is　robust　to　view　variations　and　has　a　high　authentication　rate.　©　2014　Chinese　Association　of　Automation.