Although　many　approaches　for　iris　recognition　have　been　proposed　in　the　last　few　years,　few　of　them　can　perfect　well　in　various　image　qualities.　In　this　paper,　a　novel　method　for　iris　recognition　based　on　feature　fusion　is　presented.　Global　and　local　iris　features　are　extracted　to　improve　the　robustness　of　iris　recognition　for　the　various　image　quality.　To　represent　the　iris　pattern　efficiently,　the　global　features　are　obtained　from　the　2D　log　Gabor　wavelet　filter　and　the　local　features　are　fused　to　complete　the　iris　recognition.　The　weighting　Euclidean　distance　and　the　Hamming　distance　are　applied　to　match　and　classify.　In　addition,　the　thresholds　are　set　up　to　reduce　the　computation　time　of　match,　and　to　increase　robust　iris　recognition.　Experimental　results　that　confirm　the　benefits　of　using　the　proposed　method　are　reported.