As　an　important　problem　in　the　field　of　computer　vision,　facial　expression　recognition　has　been　widely　used　in　many　fields　such　as　human-computer　interaction　and　emotion　recognition.　This　paper　proposes　a　dual-branch　parallel　facial　expression　recognition　network　that　integrates　Convolutional　Neural　Network　(CNN)　and　Transformer.　By　consolidating　local　and　global　information　obtained　from　the　dual　branches,　the　network　effectively　combines　subtle　local　variations　and　overall　contextual　features　of　facial　expressions.　The　fused　feature　representation　demonstrates　enhanced　representativeness　and　discriminative　power,　leading　to　a　notable　improvement　in　facial　expression　recognition　accuracy.　In　addressing　the　challenges　posed　by　inter-class　similarity　and　annotation　ambiguity,　the　Erasing　Attention　Consistency　(EAC)　mechanism　is　employed.　This　mechanism　effectively　prevents　the　model　from　memorizing　noisy　samples,　thereby　enhancing　robustness　against　noisy　labels.　Additionally,　the　focal　loss　function　is　utilized　to　rectify　imbalances　within　data　samples.　Experiments　conducted　on　RAF-DB　datasets　with　labels　containing　noise　reveal　that　the　proposed　methodology　achieves　accuracies　of　87.65%　and　86.05%　at　10%　and　20%　noise　rate,　respectively.　This　substantiates　the　effectiveness　of　the　proposed　approach.　©　2024　IEEE.