Automatic　face　recognition　is　a　challenge　task,　especially　working　in　practical　uncontrolled　environment.　Over　the　past　two　decades,　numerous　innovative　ideas　and　effective　processing　approaches　have　been　proposed　and　developed,　e.　g.　different　normalization　techniques,　intrinsic　feature　extractions　and　representation　schemes,　machine　learning　methods　and　recognition　mechanisms　etc.　Processing　with　integrating　multiple　cues　is　an　effective　approach　for　upgrading　recognition　performance.　A　multiple　stage　with　integrating　Harr,　gradient　and　cuvelete　features　for　locating　eye　center　approach　is　presented.　The　integration　based　approach　achieved　high　location　accuracy　and　outperforms　other　state　of　the　art　methods.　A　face　recognition　classifier　using　integrating　Gabor　feature　representation　and　curvelet　feature　representation　is　also　presented.　The　experiment　results　show　that　the　fusion　processing　can　reduce　error　rate　about　30%,　compared　with　using　Gabor　feature　or　curvelet　feature　alone.