Large　amount　of　labeled　training　data　is　required　to　develop　robust　and　effective　facial　expression　analysis　methods.　However,　obtaining　such　data　is　typically　a　tedious　and　time-consuming　task　that　is　proportional　to　the　size　of　the　database.　Due　to　the　rapid　advance　of　Internet　and　Web　technologies,　it　is　now　feasible　to　collect　a　tremendous　number　of　images　with　potential　label　information　at　a　low　cost　of　human　effort.　Therefore,　this　paper　proposes　a　framework　to　collect　realistic　facial　expression　images　from　the　web　so　as　to　promote　further　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　the　query　keyword.　We　present　a　SVM　based　active　learning　approach　to　selecting　relevant　images　from　noisy　image　search　results.　The　resulting　database　is　more　diverse　with　more　sample　images,　compared　with　other　well　established　facial　expression　databases　CK　and　JAFFE.　Experimental　results　demonstrate　that　the　generalization　of　our　web　based　database　outperforms　those　two　existing　databases.　It　is　anticipated　that　further　research　on　facial　expression　recognition　or　even　affective　computing　will　not　be　restricted　to　traditional　7　categories　only.　©　2010　IEEE.