Bio-Inspired Deep Attribute Learning Towards Facial Aesthetic Prediction

Computational prediction of facial aesthetics has attracted ever-increasing research focus, which has wide range of prospects in multimedia applications. The key challenge lies in extracting discriminative and perception-aware features to characterize the facial beautifulness. To this end, the existing schemes simply adopt a direct feature mapping, which relies on handcraft-designed low-level features that cannot reflect human-level aesthetic perception. In this paper, we present a systematic framework towards designing biology-inspired, discriminative representation for facial aesthetic prediction. First, we design a group of biological experiments that adopt eye tracker to identify spatial regions of interest during the facial aesthetic judgments of subjects, which forms a Bio-inspired Facial Aesthetic Ontology (Bio-FAO) and is made public available. Second, we adopt the cutting-edge convolutional neural network to train a set of Bio-inspired Attribute features, termed Bio-AttriBank, which forms a mid-level interpretable representation corresponding to the aforementioned Bio-FAO. For a given image, the facial aesthetic prediction is then formulated as a classification problem over the Bio-AttriBank descriptor responses, which well bridges the affective gap, and provides explainable evidences on why/how a face is beautiful or not. We have carried out extensive experiments on both JAFFE and FaceWarehouse datasets, with comparisons to a set of state-of-the-art and alternative approaches. Superior performance gains in the experiments have demonstrated the merits of the proposed scheme.