Recent　years　witnessed　a　surge　in　the　number　of　IoT　cameras　in　smart　cities.　In　this　article,　an　ensemble　learning-based　prediction　model　for　image　forensics　from　IoT　camera　is　proposed.　In　particular,　our　goal　is　to　obtain　human　body　measurements　from　2D　images　taken　from　two　views.　Firstly,　24　body　part　features　are　extracted　by　the　DensePose　algorithm　from　the　two　views.　Secondly,　the　features　of　the　upper　body　part　are　integrated　with　height　and　body　weight　features.　Ensemble　learning　is　then　performed　with　the　LightGBM　algorithm　and　a　regression　prediction　model　is　constructed.　The　proposed　noncontact　image　prediction　method　is　simple　and　workable.　Its　feasibility　and　validity　are　verified　on　an　experimental　dataset.　Experimental　results　demonstrate　that　the　proposed　method　is　highly　reliable　in　the　size　prediction　of　different　body　parts.　Specifically,　the　mean　absolute　errors　of　chest　circumference,　waistline　and　hip　circumference　are　about　2.5　cm,　while　the　mean　absolute　errors　of　other　predictions　are　about　1　cm.