Automatic　face　recognition　is　a　challenge　task,　especially　working　in　practical　uncontrolled　environments.　Over　the　past　two　decades,　numerous　innovative　ideas　and　effective　processing　approaches　had　been　proposed　and　developed,　e.g.　various　normalization　techniques,　intrinsic　feature　extractions　and　representation　schemes,　machine　learning　methods　and　recognition　mechanisms　etc.　Those　approaches　based　on　different　principles　had　been　shown　possessing　varying　degrees　of　effectiveness　in　different　aspects.　It　is　expected　that　the　techniques　of　information　fusion　with　integrating　the　advantages　of　existing　methods　will　boost　the　recognition　performance.　This　paper　deals　with　developing　effective　approaches　for　face　recognition　using　information　fusion　techniques　based　on　integrating　multiple　cues.　The　multiple　stage　integrating　techniques　dedicated　to　localization　of　landmark　points　and　pose　estimation　were　presented.　The　precise　data　of　localization　of　landmarks　and　pose　estimation　provide　the　essential　geometry　basics　for　further　processing.　A　face　recognition　classifier　scheme　with　integration　of　multiple　feature　representation　and　multiple　block　region　scores　is　also　proposed.　The　experiment　results　show　that　the　proposed　approach　can　reduce　equal　error　rate　EER　significantly,　compared　with　using　single　feature　and　single　block　representations.　The　proposed　approach　had　been　shown　possessing　the　best　performance　in　participating　MCFR2011　competition.　©　2013　Springer　Science+Business　Media　New　York.