Multi-label recognition of facial body constitution via MLP-like deep networks with non-equilibrium weight loss

In multi-label image classification tasks, uncertainty in the number of sample labels is a common problem, which can lead to the existence of a unbalance in the labels of samples. Facial body constitution recognition is one of the important methods of traditional Chinese medicine (TCM) body constitution recognition, which has important significance for body constitution conditioning and disease prevention. Based on the sandwich constitution of the TCM body constitution theory, the multi-label constitution corresponding to the facial image is regarded as a multi-label recognition task. A new multi-label body constitution (MLBC) dataset is proposed using facial images in this paper. We propose a novel deep network via the MLP-like architectures, including Wave-MLP, Vip, and Cycle-MLP, for the multi-label facial body constitution recognition task with a non-equilibrium weight (NEBW) loss function. The experiments compare five commonly used multi-label classification loss functions for analyzing their recognition performance. The results show the effectiveness of the proposed NEBW loss function on this task. Notably, compared with the benchmark binary cross-entropy (BCE) loss function, the three kinds of MLP-like models improve by 1.50, 1.78, and 1.44 percentage points from Acc, respectively; the improvements are 1.09, 2.00, and 2.19 percentage points from mAP, respectively. And the improvements are 1.06, 2.43, and 0.77 percentage points from AUC, respectively.