Emerging　applications　are　imposing　challenges　for　incorporating　fairness　constraints　into　k-center　clustering　in　the　streaming　setting.　Different　from　the　traditional　k-center　problem,　the　fairness　constraints　require　that　the　input　points　be　divided　into　disjoint　groups　and　the　number　of　centers　from　each　group　is　constrained　by　a　given　upper　bound.　Moreover,　observing　the　applications　of　fair　k-center　inmassive　datasets,　we　consider　the　problem　in　the　streaming　setting,　where　the　data　points　arrive　in　a　streaming　manner　that　each　point　can　be　processed　at　its　arrival.　As　themain　contributions,　we　propose　a　two-pass　streaming　algorithm　for　the　fair　k-center　problem　with　two　groups,　achieving　an　approximation　ratio　of　3　+　epsilon　and　consuming　only　O(k　log　n)　memory　and　O(k)　update　time,　matching　the　state-of-art　ratio　for　the　offline　setting.　Then,　we　show　that　the　algorithm　can　be　easily　improved　to　a　one-pass　streaming　algorithm　with　an　approximation　ratio　of　7+　epsilon　and　the　same　memory　complexity　and　update　time.　Moreover,　we　show　that　our　algorithm　can　be　simply　tuned　to　solve　the　case　with　an　arbitrary　number　of　groups　while　achieving　the　same　ratio　and　space　complexity.　Lastly,　we　carried　out　extensive　experiments　to　evaluate　the　practical　performance　of　our　algorithm　compared　with　the　state-of-the-art　algorithms.