Facial　Beauty　Prediction　(FBP)　is　a　significant　pattern　recognition　task　that　aims　to　achieve　consistent　facial　attractiveness　assess-ment　with　human　perception.　Currently,　Convolutional　Neural　Networks　(CNNs)　have　become　the　mainstream　method　for　FBP.　The　training　ob-jective　of　most　conventional　CNNs　is　usually　to　learn　static　convolution　kernels,　which,　however,　makes　the　network　quite　difficult　to　capture　global　attentive　information,　and　thus　usually　ignores　the　key　facial　regions,　e.g.,　eyes,　and　nose.　To　tackle　this　problem,　we　devise　a　new　convolution　man-ner,　Dynamic　Attentive　Convolution　(DyAttenConv),　which　integrates　the　dynamic　and　attention　mechanism　into　convolution　in　kernel-level,　with　the　aim　of　enforcing　the　convolution　kernels　adapted　to　each　face　dynamically.　DyAttenConv　is　a　plug-and-play　module　that　can　be　flexibly　combined　with　existing　CNN　architectures,　making　the　acquisition　of　the　beauty-related　features　more　globally　and　attentively.　Extensive　ablation　studies　show　that　our　method　is　superior　to　other　fusion　and　attention　mechanisms,　and　the　comparison　with　other　state-of-the-arts　also　demonstrates　the　effectiveness　of　DyAttenConv　on　facial　beauty　prediction　task.　Copyright　©　2024　The　Institute　of　Electronics,　Information　and　Communication　Engineers.