Blended Emotion in-the-Wild: Multi-label Facial Expression Recognition Using Crowdsourced Annotations and Deep Locality Feature Learning

Abstract

Comprehending different categories of facial expressions plays a great role in the design of computational model analyzing human perceived and affective state. Authoritative studies have revealed that facial expressions in human daily life are in multiple or co-occurring mental states. However, due to the lack of valid datasets, most previous studies are still restricted to basic emotions with single label. In this paper, we present a novel multi-label facial expression database, RAF-ML, along with a new deep learning algorithm, to address this problem. Specifically, a crowdsourcing annotation of 1.2 million labels from 315 participants was implemented to identify the multi-label expressions collected from social network, then EM algorithm was designed to filter out unreliable labels. For all we know, RAF-ML is the first database in the wild that provides with crowdsourced cognition for multi-label expressions. Focusing on the ambiguity and continuity of blended expressions, we propose a new deep manifold learning network, called Deep Bi-Manifold CNN, to learn the discriminative feature for multi-label expressions by jointly preserving the local affinity of deep features and the manifold structures of emotion labels. Furthermore, a deep domain adaption method is leveraged to extend the deep manifold features learned from RAF-ML to other expression databases under various imaging conditions and cultures. Extensive experiments on the RAF-ML and other diverse databases (JAFFE, CK$$+$$+, SFEW and MMI) show that the deep manifold feature is not only superior in multi-label expression recognition in the wild, but also captures the elemental and generic components that are effective for a wide range of expression recognition tasks.