text: Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either spontaneous' or posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.
  @cite_37: 
  @cite_29: Asymmetries of the smiling facial movement were more frequent in deliberate imitations than spontaneous emotional expressions. When asymmetries did occur they were usually stronger on the left side of the face if the smile was deliberate. Asymmetrical emotional expressions, however, were about equally divided between those stronger on the left side of the face and those stronger on the right. Similar findings were obtained for the actions involved in negative emotions, but a small data base made these results tentative.
  @cite_1: Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics.
  @cite_19: We investigated movement differences between deliberately posed and spontaneously occurring smiles and eyebrow raises during a videotaped interview that included a facial movement assessment. Using automated facial image analysis, we quantified lip corner and eyebrow movement during periods of visible smiles and eyebrow raises and compared facial movement within participants. As in an earlier study, maximum speed of movement onset was greater in deliberate smiles. Maximum speed and amplitude were greater and duration shorter in deliberate compared to spontaneous eyebrow raises. Asymmetry of movement did not differ within participants. Similar patterns contrasting deliberate and spontaneous movement in both smiles and eyebrow raises suggest a common pattern of signaling for spontaneous facial displays.
  @cite_10: We investigated the value of the Duchenne (D) smile as a spontaneous sign of felt enjoyment. Participants either smiled spontaneously in response to amusing material (spontaneous condition) or were instructed to pose a smile (deliberate condition). Similar amounts of D and non-Duchenne (ND) smiles were observed in these 2 conditions (Experiment 1). When subsets of these smiles were presented to other participants, they generally rated spontaneous and deliberate D and ND smiles differently. Moreover, they distinguished between D smiles of varying intensity within the spontaneous condition (Experiment 2). Such a differentiation was also made when seeing the upper or lower face only (Experiment 3), but was impaired for static compared with dynamic displays (Experiment 4). The predictive value of the D smile in these judgment studies was limited compared with other features such as asymmetry, apex duration, and nonpositive facial actions, and was only significant for ratings of the upper face and static displays. These findings raise doubts about the reliability and validity of the D smile and question the usefulness of facial descriptions in identifying true feelings of enjoyment.
summary: In early years of research, it was thought that the morphological features of the face were good indicators of a spontaneous smile. The Facial Action Coding System (FACS) @cite_37 defines Action Units (AU), which are the contraction or relaxation of one or more muscles. It is commonly used to code facial expressions, and can be used to identify emotions. In FACS, a smile corresponds to AU12, which is the contraction of the zygomatic major muscle that raises the lip corners. A genuine smile of joy is thought to include AU6, also known as the Duchenne Marker, which is the contraction of the orbicularis oculi (pars lateralis) muscle that raises the cheek, narrows the eye aperture, and forms wrinkles on the external sides of the eyes. However, recent research casts doubt on the reliability of Duchenne marker in identifying true feelings of enjoyment @cite_10 . Another possible marker of spontaneous smiles is the symmetry of the smile. Initial studies claim that smile symmetry is a factor in identifying spontaneous smiles, where spontaneous smiles are more symmetrical than posed smiles @cite_29 . However, later studies report no significant differences of symmetry @cite_1 @cite_19 .