Face　recognition　based　on　deep　convolutional　neural　networks　(CNN)　shows　superior　accuracy　performance　attributed　to　the　high　discriminative　features　extracted.　Yet,　the　security　and　privacy　of　the　extracted　features　from　deep　learning　models　(deep　features)　have　often　been　overlooked.　This　paper　proposes　the　reconstruction　of　face　images　from　deep　features　without　accessing　the　CNN　network　configurations　as　a　constrained　optimization　problem.　Such　optimization　minimizes　the　distance　between　the　features　extracted　from　the　original　face　image　and　the　reconstructed　face　image.　Instead　of　directly　solving　the　optimization　problem　in　the　image　space,　we　innovatively　reformulate　the　problem　by　looking　for　a　latent　vector　of　a　generative　adversarial　networks　(GAN)　generator,　then　use　it　to　generate　the　face　image.　The　GAN　generator　serves　a　dual　role　in　this　novel　framework,　i.e.,　face　distribution　constraint　of　the　optimization　goal　and　a　face　generator.　To　solve　this　optimization　problem,　We　present　an　optimization　approach　based　on　a　Genetic　Algorithm.　On　top　of　the　novel　optimization　task,　we　also　propose　an　attack　pipeline　to　impersonate　the　target　user　based　on　the　generated　face　image.　Our　results　show　that　the　generated　face　images　can　achieve　a　state-of-the-art　successful　attack　rate　of　99.33%　on　Labeled　Faces　in　the　Wild　(LFW)　under　type-I　attack　at　a　false　accept　rate　of　0.1%.　Our　work　sheds　light　on　biometric　deployment　to　meet　privacy-preserving　and　security　policies.　©　2022　Elsevier　Ltd