Ezekiel Olayide Tolulope

Facial emotion recognition for human-robot interaction is usually judged by clean-set accuracy, yet a robot that maps an emotion estimate to an action also needs to know whether that estimate can be trusted. Comparing five pipelines on a five-class FER2013 subset, the strongest deep residual CNN reaches 0.760 accuracy and 0.753 macro-F1 but collapses with every other trained architecture under gradient attacks: FGSM drops all four below 0.16 accuracy at epsilon 0.01, and PGD drives them near zero at epsilon 0.02 and above. The attacked predictions stay confident, with mean confidence 0.964 under PGD and 96.0% still above a 0.70 threshold after temperature scaling, so thresholding the maximum softmax probability does not separate legitimate from adversarial predictions.

Facial emotion recognition for human-robot interaction is usually judged by clean-set accuracy, yet a robot that maps an emotion estimate to an action also needs to know whether that estimate can be trusted. Comparing five pipelines on a five-class FER2013 subset, the strongest deep residual CNN reaches 0.760 accuracy and 0.753 macro-F1 but collapses with every other trained architecture under gradient attacks: FGSM drops all four below 0.16 accuracy at epsilon 0.01, and PGD drives them near zero at epsilon 0.02 and above. The attacked predictions stay confident, with mean confidence 0.964 under PGD and 96.0% still above a 0.70 threshold after temperature scaling, so thresholding the maximum softmax probability does not separate legitimate from adversarial predictions.