Registration　algorithms　performed　on　point　clouds　or　range　images　of　face　scans　have　been　successfully　used　for　automatic　3D　face　recognition　under　expression　variations,　but　have　rarely　been　investigated　to　solve　pose　changes　and　occlusions　mainly　since　that　the　basic　landmarks　to　initialize　coarse　alignment　are　not　always　available.　Recently,　local　feature-based　SIFT-like　matching　proves　competent　to　handle　all　such　variations　without　registration.　In　this　paper,　towards　3D　face　recognition　for　real-life　biometric　applications,　we　significantly　extend　the　SIFT-like　matching　framework　to　mesh　data　and　propose　a　novel　approach　using　fine-grained　matching　of　3D　keypoint　descriptors.　First,　two　principal　curvature-based　3D　keypoint　detectors　are　provided,　which　can　repeatedly　identify　complementary　locations　on　a　face　scan　where　local　curvatures　are　high.　Then,　a　robust　3D　local　coordinate　system　is　built　at　each　keypoint,　which　allows　extraction　of　pose-invariant　features.　Three　keypoint　descriptors,　corresponding　to　three　surface　differential　quantities,　are　designed,　and　their　feature-level　fusion　is　employed　to　comprehensively　describe　local　shapes　of　detected　keypoints.　Finally,　we　propose　a　multi-task　sparse　representation　based　fine-grained　matching　algorithm,　which　accounts　for　the　average　reconstruction　error　of　probe　face　descriptors　sparsely　represented　by　a　large　dictionary　of　gallery　descriptors　in　identification.　Our　approach　is　evaluated　on　the　Bosphorus　database　and　achieves　rank-one　recognition　rates　of　96.56,　98.82,　91.14,　and　99.21　%　on　the　entire　database,　and　the　expression,　pose,　and　occlusion　subsets,　respectively.　To　the　best　of　our　knowledge,　these　are　the　best　results　reported　so　far　on　this　database.　Additionally,　good　generalization　ability　is　also　exhibited　by　the　experiments　on　the　FRGC　v2.0　database.