Two-dimensional　principal　component　analysis　(2DPCA)　has　been　widely　used　to　extract　image　features.　As　opposed　to　PCA,　2DPCA　directly　treats　2D　matrices　to　extract　image　features　instead　of　transforming　2D　matrices　into　vectors.　However,　the　classical　2DPCA　based　on　F-norm　square　is　sensitive　to　noise.　To　handle　this　problem,　2DPCAs　based　on　l(1)-norm,　lp-norm,　and　other　norms　have　been　studied.　In　this　paper,　as　a　further　development,　2DPCA　based　on　Tl-1　criterion　is　proposed,　referred　as　2DPCA-Tl-1.　Notice　that,　different　from　some　norms　used　before,　Tl-1　criterion　is　bounded　and　Lipschitz-continuous.　So　it　can　be　expected　that　our　2DPCA-Tl-1　should　be　more　robust.　In　fact,　the　experimental　results　have　shown　that　its　performance　is　superior　to　that　of　classical　2DPCA,　2DPCA-L1,　2DPCAL1-S,　N-2-DPCA,　G2DPCA,　and　Angle-2DPCA.