sustaining_gazes/matlab_runners/Action Unit Experiments/results/BP4D_valid_res_class.txt

AU1 class, Precision - 0.501, Recall - 0.521, F1 - 0.511
AU2 class, Precision - 0.379, Recall - 0.451, F1 - 0.412
AU4 class, Precision - 0.459, Recall - 0.503, F1 - 0.480
AU6 class, Precision - 0.738, Recall - 0.774, F1 - 0.755
AU7 class, Precision - 0.762, Recall - 0.732, F1 - 0.747
AU10 class, Precision - 0.847, Recall - 0.842, F1 - 0.844
AU12 class, Precision - 0.888, Recall - 0.816, F1 - 0.850
AU14 class, Precision - 0.547, Recall - 0.807, F1 - 0.652
AU15 class, Precision - 0.402, Recall - 0.416, F1 - 0.409
AU17 class, Precision - 0.632, Recall - 0.592, F1 - 0.611
AU23 class, Precision - 0.338, Recall - 0.597, F1 - 0.431
A lot of changes: - New AU recognition models trained on extra datasets - Bosphorus, UNBC, FERA2011 - Cleaner and clearer separation of static and dynamic AU models - AU training code cleaned up and instructions added - bug fixes with median feature computation - AU prediction correction (smoothing and shifting) with post processing 2016-07-22 13:35:50 +00:00			`AU1 class, Precision - 0.501, Recall - 0.521, F1 - 0.511`
			`AU2 class, Precision - 0.379, Recall - 0.451, F1 - 0.412`
			`AU4 class, Precision - 0.459, Recall - 0.503, F1 - 0.480`
			`AU6 class, Precision - 0.738, Recall - 0.774, F1 - 0.755`
			`AU7 class, Precision - 0.762, Recall - 0.732, F1 - 0.747`
			`AU10 class, Precision - 0.847, Recall - 0.842, F1 - 0.844`
			`AU12 class, Precision - 0.888, Recall - 0.816, F1 - 0.850`
			`AU14 class, Precision - 0.547, Recall - 0.807, F1 - 0.652`
			`AU15 class, Precision - 0.402, Recall - 0.416, F1 - 0.409`
			`AU17 class, Precision - 0.632, Recall - 0.592, F1 - 0.611`
			`AU23 class, Precision - 0.338, Recall - 0.597, F1 - 0.431`