The　convolution　neural　network　(CNN)　has　achieved　state-of-the-art　performance　in　many　computer　vision　applications　e.g.,　classification,　recognition,　detection,　etc.　However,　the　global　optimization.　of　CNN　training　is　still　a　problem.　Fast　classification　and　training　play　a　key　role　in　the　development　of　the　CNN.　We　hypothesize　that　the　smoother　and　optimized　the　training　of　a　CNN　goes,　the　more　efficient　the　end　result　becomes.　Therefore,　in　this　paper,　we　implement　a　modified　resilient　backpropagation　(MRPROP)　algorithm　to　improve　the　convergence　and　efficiency　of　CNN　training.　Particularly,　a　tolerant　band　is　introduced　to　avoid　network　overtraining,　which　is　incorporated　with　the　global　best　concept　for　weight　updating　criteria　to　allow　the　training　algorithm　of　the　CNN　to　optimize　its　weights　more　swiftly　and　precisely.　For　comparison,　we　present　and　analyze　four　different　training　algorithms　for　CNN　along　with　MRPROP,　i.e.,　resilient　backpropagation　(RPROP),　Levenberg-Marquardt　(LM),　conjugate　gradient　(CG),　and　gradient　descent　with　momentum　(GDM).　Experimental　results　showcase　the　merit　of　the　proposed　approach　on　a　public　face　and　skin　dataset.