A　large　amount　of　labeled　training　data　is　required　to　develop　effective　and　robust　facial　expression　analysis　methods.　However,　obtaining　such　data　is　typically　a　tedious　and　time-consuming　task.　With　a　rapid　advance　of　the　Internet　and　Web　technologies,　it　has　been　feasible　to　collect　a　large　number　of　images　with　label　information　at　a　low　cost　of　human　efforts.　In　this　paper,　we　propose　a　search　based　framework　to　collect　realistic　facial　expression　images　from　the　Web　so　as　to　further　advance　research　on　robust　facial　expression　recognition.　Due　to　the　limitation　of　current　commercial　web　search　engines,　a　large　fraction　of　returned　images　is　not　related　to　a　given　query　keyword.　We　present　a　Support　Vector　Machine　(SVM)　based　active　learning　approach　for　selecting　relevant　images　from　noisy　image　search　results.　The　resulting　dataset　is　more　diverse　with　more　sample　images　per　expression　compared　to　other　well　established　facial　expression　datasets　such　as　CK　and　JAFFE.　In　addition,　a　novel　facial　expression　feature　based　on　the　state-of-the-art　Weber　Local　Descriptor　(WLD)　and　histogram　contextualization　is　proposed　to　handle　such　a　challenging　dataset.　Comprehensive　experimental　results　demonstrate　that　our　web　based　dataset　is　capable　of　resembling　more　closely　to　the　real　world　conditions　compared　to　the　CK　and　JAFFE　datasets,　and　our　proposed　feature　is　more　effective　than　the　existing　widely　used　features.　Crown　Copyright　(C)　2013　Published　by　Elsevier　Ltd.　All　rights　reserved.