This　paper　studies　exaggerated　facial　shapes　in　addition　to　original　facial　shapes　to　assist　3D　Facial　Expression　Recognition　(FER).　We　propose　a　Poisson　equation　based　approach　to　exaggerate　facial　shape　characteristics　to　highlight　expression　clues　that　are　latent　in　original　facial　surfaces　but　useful　for　recognizing　expressions.　To　validate　this　idea,　we　exploit　two　off-the-shelf　descriptors　that　reach　state　of　the　art　performance　in　3D　FER,　namely　Geometric　Scattering　Representation　(GSR)　and　Multi-Scale　Local　Normal　Patterns　(MS-LNPs)　for　expression-related　feature　extraction,　and　adopt　early　fusion　to　combine　the　credits　of　the　original　surface　and　the　enhanced　one,　followed　by　the　SVMs　and　Multiple　Kernel　Learning　(MKL)　classifiers.　The　accuracy　gain　of　two　features　achieved　on　BU-3DFE　is　0.8%　and　1.3%　respectively.　Such　results　show　that　the　exaggerated　faces　are　complementary　to　the　original　faces　in　discriminating　different　facial　expressions　in　the　3D　domain.　©　2017,　Springer　International　Publishing　AG.