In this paper, we proposed a novel Identity-free conditional Generative Adversarial Network (IF-GAN) to explicitly reduce inter-subject variations for facial expression recognition. Specifically, for any given input face image, a conditional generative model was developed to transform an average neutral face, which is calculated from various subjects showing neutral expressions, to an average expressive face with the same expression as the input image. Since the transformed images have the same synthetic "average" identity, they differ from each other by only their expressions and thus, can be used for identity-free expression classification. In this work, an end-to-end system was developed to perform expression transformation and expression recognition in the IF-GAN framework. Experimental results on three facial expression datasets have demonstrated that the proposed IF-GAN outperforms the baseline CNN model and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.
Approaches @cite_25 @cite_19 were designed to learn discriminative features for facial expression recognition by reducing the intra-class variations while increasing the inter-class differences simultaneously. More recently, there are a few approaches focusing on explicitly improving person-independent facial expression recognition. An Identity-Aware CNN (IACNN) @cite_15 was proposed to alleviate variations introduced by identity-related information using an expression-sensitive contrastive loss and an identity-sensitive contrastive loss. However, the contrastive loss suffers from drastic data expansion when constructing image pairs from the training data. An Identity-Adaptive Generation (IA-gen) method @cite_17 was developed to generate person-dependent facial expression images such that any given input facial image is transferred to six expressive images of the same subject using six conditional GANs (cGANs). Then, expression classification is performed by comparing the input image with the six generated expressive images. De-expression Residue Learning (DeRL) @cite_31 also utilized the cGAN to synthesize a neutral facial image of the same identity from any input expressive image, while the person-independent expression information can be extracted from the intermediate layers of the generative model. However, the aforementioned cGAN-based models @cite_17 @cite_31 are not end-to-end networks and suffer from expensive computational cost.
@cite_31 @cite_19 @cite_15 @cite_25 @cite_17
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Subject variation is a challenging issue for fa- cial expression recognition, especially when handling unseen subjects with small-scale lableled facial expression databases. Although transfer learning has been widely used to tackle the problem, the performance degrades on new data. In this paper, we present a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images. First of all, we train conditional generative models to generate six prototypic facial expressions from any given query face image while keeping the identity related information unchanged. Generative Adversarial Networks are employed to train the conditional generative models, and each of them is designed to generate one of the prototypic facial expression images. Second, a regular CNN (FER-Net) is fine- tuned for expression classification. After the corresponding prototypic facial expressions are regenerated from each facial image, we output the last FC layer of FER-Net as features for both the input image and the generated images. Based on the minimum distance between the input image and the generated expression images in the feature space, the input image is classified as one of the prototypic expressions consequently. Our proposed method can not only alleviate the influence of inter-subject variations, but will also be flexible enough to integrate with any other FER CNNs for person-independent facial expression recognition. Our method has been evaluated on CK+, Oulu-CASIA, BU-3DFE and BU-4DFE databases, and the results demonstrate the effectiveness of our proposed method. We propose a semi-supervised approach to solve the task of emotion recognition in 2D face images using recent ideas in deep learning for handling the factors of variation present in data. An emotion classification algorithm should be both robust to (1) remaining variations due to the pose of the face in the image after centering and alignment, (2) the identity or morphology of the face. In order to achieve this invariance, we propose to learn a hierarchy of features in which we gradually filter the factors of variation arising from both (1) and (2). We address (1) by using a multi-scale contractive convolutional network (CCNET) in order to obtain invariance to translations of the facial traits in the image. Using the feature representation produced by the CCNET, we train a Contractive Discriminative Analysis (CDA) feature extractor, a novel variant of the Contractive Auto-Encoder (CAE), designed to learn a representation separating out the emotion-related factors from the others (which mostly capture the subject identity, and what is left of pose after the CCNET). This system beats the state-of-the-art on a recently proposed dataset for facial expression recognition, the Toronto Face Database, moving the state-of-art accuracy from 82.4 to 85.0 , while the CCNET and CDA improve accuracy of a standard CAE by 8 . In this paper, we proposed a novel Identity-free conditional Generative Adversarial Network (IF-GAN) to explicitly reduce inter-subject variations for facial expression recognition. Specifically, for any given input face image, a conditional generative model was developed to transform an average neutral face, which is calculated from various subjects showing neutral expressions, to an average expressive face with the same expression as the input image. Since the transformed images have the same synthetic "average" identity, they differ from each other by only their expressions and thus, can be used for identity-free expression classification. In this work, an end-to-end system was developed to perform expression transformation and expression recognition in the IF-GAN framework. Experimental results on three facial expression datasets have demonstrated that the proposed IF-GAN outperforms the baseline CNN model and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition. In this paper, we propose a geometry-contrastive generative adversarial network GC-GAN for generating facial expression images conditioned on geometry information. Specifically, given an input face and a target expression designated by a set of facial landmarks, an identity-preserving face can be generated guided by the target expression. In order to embed facial geometry onto a semantic manifold, we incorporate contrastive learning into conditional GANs. Experiment results demonstrate that the manifold is sensitive to the changes of facial geometry both globally and locally. Benefited from the semantic manifold, dynamic smooth transitions between different facial expressions are exhibited via geometry interpolation. Furthermore, our method can also be applied in facial expression transfer even there exist big differences in face shape between target faces and driving faces.