Person　re-identification　(Re-ID)　is　to　retrieve　a　particular　person　captured　by　different　cameras,　which　is　of　great　significance　for　security　surveillance　and　pedestrian　behavior　analysis.　However,　due　to　the　large　intra-class　variation　of　a　person　across　cameras,　e.g.,　occlusions,　illuminations,　viewpoints,　and　poses,　Re-ID　is　still　a　challenging　task　in　the　field　of　computer　vision.　In　this　paper,　to　attack　the　issues　concerning　with　intra-class　variation,　we　propose　a　coarse-to-fine　Re-ID　framework　with　the　incorporation　of　auxiliary-domain　classification　(ADC)　and　second-order　information　bottleneck　(2O-IB).　In　particular,　as　an　auxiliary　task,　ADC　is　introduced　to　extract　the　coarse-grained　essential　features　to　distinguish　a　person　from　miscellaneous　backgrounds,　which　leads　to　the　effective　coarse-　and　fine-grained　feature　representations　for　Re-ID.　On　the　other　hand,　to　cope　with　the　redundancy,　irrelevance,　and　noise　contained　in　the　Re-ID　features　caused　by　intra-class　variations,　we　integrate　2O-IB　into　the　network　to　compress　and　optimize　the　features,　without　increasing　additional　computation　overhead　during　inference.　Experimental　results　demonstrate　that　our　proposed　method　significantly　reduces　the　neural　network　output　variance　of　intra-class　person　images　and　achieves　the　superior　performance　to　state-of-the-art　methods.