Facial　expression　recognition　is　an　important　research　issue　in　the　pattern　recognition　field.　In　this　paper,　we　intend　to　present　a　novel　framework　for　facial　expression　recognition　to　automatically　distinguish　the　expressions　with　high　accuracy.　Especially,　a　high-dimensional　feature　composed　by　the　combination　of　the　facial　geometric　and　appearance　features　is　introduced　to　the　facial　expression　recognition　due　to　its　containing　the　accurate　and　comprehensive　information　of　emotions.　Furthermore,　the　deep　sparse　autoencoders　(DSAE)　are　established　to　recognize　the　facial　expressions　with　high　accuracy　by　learning　robust　and　discriminative　features　from　the　data.　The　experiment　results　indicate　that　the　presented　framework　can　achieve　a　high　recognition　accuracy　of　95.79%　on　the　extended　Cohn–Kanade　(CK+)　database　for　seven　facial　expressions,　which　outperforms　the　other　three　state-of-the-art　methods　by　as　much　as　3.17%,　4.09%　and　7.41%,　respectively.　In　particular,　the　presented　approach　is　also　applied　to　recognize　eight　facial　expressions　(including　the　neutral)　and　it　provides　a　satisfactory　recognition　accuracy,　which　successfully　demonstrates　the　feasibility　and　effectiveness　of　the　approach　in　this　paper.　©　2017　Elsevier　B.V.