The　development　of　machine　learning　technology　and　visual　sensors　is　promoting　the　wider　applications　of　face　recognition　into　our　daily　life.　However,　if　the　face　features　in　the　servers　are　abused　by　the　adversary,　our　privacy　and　wealth　can　be　faced　with　great　threat.　Many　security　experts　have　pointed　out　that,　by　3-D-printing　technology,　the　adversary　can　utilize　the　leaked　face　feature　data　to　masquerade　others　and　break　the　E-bank　accounts.　Therefore,　in　this　paper,　we　propose　a　lightweight　privacy-preserving　adaptive　boosting　(AdaBoost)　classification　framework　for　face　recognition　(POR)　based　on　the　additive　secret　sharing　and　edge　computing.　First,　we　improve　the　current　additive　secret　sharing-based　exponentiation　and　logarithm　functions　by　expanding　the　effective　input　range.　Then,　by　utilizing　the　protocols,　two　edge　servers　are　deployed　to　cooperatively　complete　the　ensemble　classification　of　AdaBoost　for　face　recognition.　The　application　of　edge　computing　ensures　the　efficiency　and　robustness　of　POR.　Furthermore,　we　prove　the　correctness　and　security　of　our　protocols　by　theoretic　analysis.　And　experiment　results　show　that,　POR　can　reduce　about　58%　computation　error　compared　with　the　existing　differential　privacy-based　framework.