The　probabilistic　principal　component　analysis　(PPCA)　is　built　upon　a　global　linear　mapping,　with　which　it　is　insufficient　to　model　complex　data　variation.　This　paper　proposes　a　mixture　of　bilateral-projection　probabilistic　principal　component　analysis　model　(mixB2DPPCA)　on　2D　data.　With　multi-components　in　the　mixture,　this　model　can　be　seen　as　a　＇soft＇　cluster　algorithm　and　has　capability　of　modeling　data　with　complex　structures.　A　Bayesian　inference　scheme　has　been　proposed　based　on　the　variational　EM　(Expectation-Maximization)　approach　for　learning　model　parameters.　Experiments　on　some　publicly　available　databases　show　that　the　performance　of　mixB2DPPCA　has　been　largely　improved,　resulting　in　more　accurate　reconstruction　errors　and　recognition　rates　than　the　existing　PCA-based　algorithms.