Understanding　facial　expressions　is　a　fundamental　problem　in　affective　computing　that　has　the　potential　to　impact　both　sides　of　a　conversation　with　a　computational　agent.　Currently　static　approaches　based　on　techniques　such　as　Gabor　transformation　represent　the　state　of　the　art　for　identifying　facial　expressions　but　remain　rather　slow　and　unreliable.　In　this　study　we　introduce　and　compare　a　dynamic　technique　based　on　optical　flow　versus　this　static　Gabor　baseline,　as　well　as　integrating　information　about　static　and　dynamic　facial　characteristics　into　novel　fused　models.　Rather　than　requiring　complex　Machine　Learning,　a　simple　and　fast　template　model　based　on　K-Means　uses　a　Hidden　Markov　Model　to　provide　context.　The　system　is　trained　and　evaluated　on　distinct　subsets　of　the　Cohn-Kanade　database　of　adult　faces　which　provides　classifications　into　six　basic　expressions.　Experimental　results　show　that　the　dynamic　expression　feature　extraction　based　on　the　optical　flow　for　facial　expression　recognition　has　considerably　improved　recognition　rate.　Fusion　of　the　optical　flow　dynamic　expression　features　with　Gabor　static　expression　feature　also　increases　recognition　rate　somewhat　versus　the　static　baseline　but　the　hybrids　tested　are　not　competitive　with　the　pure　dynamic　model.　©　2013　Asian　Network　tor　Scientific　Information.