Describable visual facial attributes are now commonplace in human biometrics and affective computing, with existing algorithms even reaching a sufficient point of maturity for placement into commercial products. These algorithms model objective facets of facial appearance, such as hair and eye color, expression, and aspects of the geometry of the face. A natural extension, which has not been studied to any great extent thus far, is the ability to model subjective attributes that are assigned to a face based purely on visual judgements. For instance, with just a glance, our first impression of a face may lead us to believe that a person is smart, worthy of our trust, and perhaps even our admiration - regardless of the underlying truth behind such attributes. Psychologists believe that these judgements are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues. But work in this direction leads to an interesting question: how do we create models for problems where there is no ground truth, only measurable behavior? In this paper, we introduce a new convolutional neural network-based regression framework that allows us to train predictive models of crowd behavior for social attribute assignment. Over images from the AFLW face database, these models demonstrate strong correlations with human crowd ratings.
Both @cite_30 and @cite_1 originally conceived of visual attributes as a development supporting object recognition, rather than a primary goal in and of itself. Faces, however, are a special case where standalone analysis supports applications in biometrics and affective computing. used facial attributes for face verification and image search @cite_4 . applied the statistical extreme value theory to facial attribute search spaces to create accurate multi-dimensional representations of attribute searches @cite_0 . modeled the relationships between different attributes to create more accurate multi-attribute searches @cite_33 . And captured the interdependencies of local face regions to increase classification accuracy @cite_16 .
@cite_30 @cite_4 @cite_33 @cite_1 @cite_0 @cite_16
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We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains. Examples of face attributes include gender, age, jaw shape, nose size, etc. The advantages of an attribute-based representation for vision tasks are manifold: They can be composed to create descriptions at various levels of specificity; they are generalizable, as they can be learned once and then applied to recognize new objects or categories without any further training; and they are efficient, possibly requiring exponentially fewer attributes (and training data) than explicitly naming each category. We show how one can create and label large data sets of real-world images to train classifiers which measure the presence, absence, or degree to which an attribute is expressed in images. These classifiers can then automatically label new images. We demonstrate the current effectiveness-and explore the future potential-of using attributes for face verification and image search via human and computational experiments. Finally, we introduce two new face data sets, named FaceTracer and PubFig, with labeled attributes and identities, respectively. We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits. Recent works have shown that facial attributes are useful in a number of applications such as face recognition and retrieval. However, estimating attributes in images with large variations remains a big challenge. This challenge is addressed in this paper. Unlike existing methods that assume the independence of attributes during their estimation, our approach captures the interdependencies of local regions for each attribute, as well as the high-order correlations between different attributes, which makes it more robust to occlusions and misdetection of face regions. First, we have modeled region interdependencies with a discriminative decision tree, where each node consists of a detector and a classifier trained on a local region. The detector allows us to locate the region, while the classifier determines the presence or absence of an attribute. Second, correlations of attributes and attribute predictors are modeled by organizing all of the decision trees into a large sum-product network (SPN), which is learned by the EM algorithm and yields the most probable explanation (MPE) of the facial attributes in terms of the region's localization and classification. Experimental results on a large data set with 22,400 images show the effectiveness of the proposed approach. Facial attribute editing aims to modify either single or multiple attributes on a face image. Since it is practically infeasible to collect images with arbitrarily specified attributes for each person, the generative adversarial net (GAN) and the encoder-decoder architecture are usually incorporated to handle this task. With the encoder-decoder architecture, arbitrary attribute editing can then be conducted by decoding the latent representation of the face image conditioned on the specified attributes. A few existing methods attempt to establish attribute-independent latent representation for arbitrarily changing the attributes. However, since the attributes portray the characteristics of the face image, the attribute-independent constraint on the latent representation is excessive. Such constraint may result in information loss and unexpected distortion on the generated images (e.g. over-smoothing), especially for those identifiable attributes such as gender, race etc. Instead of imposing the attribute-independent constraint on the latent representation, we introduce an attribute classification constraint on the generated image, just requiring the correct change of the attributes. Meanwhile, reconstruction learning is introduced in order to guarantee the preservation of all other attribute-excluding details on the generated image, and adversarial learning is employed for visually realistic generation. Moreover, our method can be naturally extended to attribute intensity manipulation. Experiments on the CelebA dataset show that our method outperforms the state-of-the-arts on generating realistic attribute editing results with facial details well preserved.