Person　re-identification　(Re-ID)　plays　a　crucial　role　in　the　domains　of　security　surveillance　and　pedestrian　behavior　analysis,　as　it　aims　to　retrieve　specific　individuals　captured　by　different　cameras.　However,　the　task　of　Re-ID　remains　immensely　challenging　in　the　field　of　computer　vision,　primarily　due　to　the　extensive　intra-class　variations　exhibited　by　individuals　across　cameras.　These　variations　include　occlusions,　illuminations,　viewpoints,　and　poses.　In　this　paper,　we　present　a　novel　Re-ID　framework　that　addresses　the　inherent　issues　related　to　intra-class　variations.　Our　proposed　approach　incorporates　both　auxiliary-domain　classification　(ADC)　and　layered　semi-second-order　information　bottleneck　(LyrS2IB)　techniques.　By　incorporating　ADC　as　an　auxiliary　task,　we　leverage　coarse-grained　essential　features　that　effectively　distinguish　individuals　from　the　background.　This　enables　the　development　of　both　coarse-　and　fine-grained　feature　representations　for　Re-ID.　Furthermore,　our　framework　integrates　LyrS2IB　to　handle　redundancy,　irrelevance,　and　noise　present　in　Re-ID　features　resulting　from　intra-class　variations.　This　integration　allows　us　to　compress　and　optimize　these　features　without　incurring　additional　computation　overhead　during　inference.　Extensive　experiments　validate　the　efficacy　of　our　proposed　method,　demonstrating　a　significant　reduction　in　the　neural　network　output　variance　of　intra-class　person　images,　firmly　establishing　the　superior　performance　of　our　approach　in　the　field　of　Re-ID.