To　improve　the　robustness　and　to　reduce　the　energy　consumption　of　facial　expression　recognition,　this　study　proposed　a　facial　expression　recognition　method　based　on　improved　deep　residual　networks　(ResNets).　Residual　learning　has　solved　the　degradation　problem　of　deep　Convolutional　Neural　Networks　(CNNs);　therefore,　in　theory,　a　ResNet　can　consist　of　infinite　number　of　neural　layers.　On　the　one　hand,　ResNets　benefit　from　better　performance　on　artificial　intelligence　(AI)　tasks,　thanks　to　its　deeper　network　structure;　meanwhile,　on　the　other　hand,　it　faces　a　severe　problem　of　energy　consumption,　especially　on　mobile　devices.　Hence,　this　study　employs　a　novel　activation　function,　the　Noisy　Softplus　(NSP),　to　replace　rectified　linear　units　(ReLU)　to　get　improved　ResNets.　NSP　is　a　biologically　plausible　activation　function,　which　was　first　proposed　in　training　Spiking　Neural　Networks　(SNNs);　thus,　NSP-trained　models　can　be　directly　implemented　on　ultra-low-power　neuromorphic　hardware.　We　built　an　18-layered　ResNet　using　NSP　to　perform　facial　expression　recognition　across　datasets　Cohn-Kanade　(CK+),　Karolinska　Directed　Emotional　Faces　(KDEF)　and　GENKI-4K.　The　results　achieved　better　antinoise　ability　than　ResNet　using　the　activation　function　ReLU　and　showed　low　energy　consumption　running　on　neuromorphic　hardware.　This　study　not　only　contributes　a　solution　for　robust　facial　expression　recognition,　but　also　consolidates　the　low　energy　cost　of　their　implementation　on　neuromorphic　devices,　which　could　pave　the　way　for　high-performance,　noise-robust　and　energy-efficient　vision　applications　on　mobile　hardware.