With　the　advancement　of　technology　and　expansion　of　broadcasting　around　the　globe　has　further　boost　up　biometric　surveillance　systems.　Pattern　recognition　is　the　key　track　in　this　area.　Convolution　neural　network　(CNN)　as　one　of　the　most　prevalent　deep　learning　algorithm　has　gain　high　reputation　in　image　features　extraction.　In　this　paper,　we　propose　few　new　twists　of　unsupervised　learning　i.e.　convolution　sparse　filter　learning　(CSFL)　to　obtain　rich　and　discriminative　features　of　an　image.　The　features　extracted　by　CSFL　algorithm　are　used　to　initialize　the　first　CNN　layer,　and　then　these　features　are　further　used　in　feed　forward　manner　by　the　CNN　to　learn　high　level　features　for　classification.　The　linear　regression　classifier　(softmax　classifier)　is　used　to　serve　as　the　output　layer　of　CNN　for　providing　the　probability　of　an　image　class.　We　present　and　examine　five　different　architectures　of　CNN　and　error　function　mean　square　error　(MSE).　The　experimental　results　on　a　public　dataset　showcase　the　merit　of　the　proposed　method.