Identity-preserving Face Recovery from Portraits

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The role of the DN is to enforce recovered faces to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches

Recovering Faces from Portraits with Auxiliary Facial Attributes

Recovering a photorealistic face from an artistic portrait is a challenging task since crucial facial details are often distorted or completely lost in artistic compositions. To handle this loss, we propose an Attribute-guided Face Recovery from Portraits (AFRP) that utilizes a Face Recovery Network (FRN) and a Discriminative Network (DN). FRN consists of an autoencoder with residual block-embedded skip-connections and incorporates facial attribute vectors into the feature maps of input portraits at the bottleneck of the autoencoder. DN has multiple convolutional and fully-connected layers, and its role is to enforce FRN to generate authentic face images with corresponding facial attributes dictated by the input attribute vectors. %Leveraging on the spatial transformer networks, FRN automatically compensates for misalignments of portraits. % and generates aligned face images. For the preservation of identities, we impose the recovered and ground-truth faces to share similar visual features. Specifically, DN determines whether the recovered image looks like a real face and checks if the facial attributes extracted from the recovered image are consistent with given attributes. %Our method can recover high-quality photorealistic faces from unaligned portraits while preserving the identity of the face images as well as it can reconstruct a photorealistic face image with a desired set of attributes. Our method can recover photorealistic identity-preserving faces with desired attributes from unseen stylized portraits, artistic paintings, and hand-drawn sketches. On large-scale synthesized and sketch datasets, we demonstrate that our face recovery method achieves state-of-the-art results.Comment: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV

Face Hallucination via Deep Neural Networks.

We firstly address aligned low-resolution (LR) face images (i.e. 16X16 pixels) by designing a discriminative generative network, named URDGN. URDGN is composed of two networks: a generative model and a discriminative model. We introduce a pixel-wise L2 regularization term to the generative model and exploit the feedback of the discriminative network to make the upsampled face images more similar to real ones. We present an end-to-end transformative discriminative neural network (TDN) devised for super-resolving unaligned tiny face images. TDN embeds spatial transformation layers to enforce local receptive fields to line-up with similar spatial supports. To upsample noisy unaligned LR face images, we propose decoder-encoder-decoder networks. A transformative discriminative decoder network is employed to upsample and denoise LR inputs simultaneously. Then we project the intermediate HR faces to aligned and noise-free LR faces by a transformative encoder network. Finally, high-quality hallucinated HR images are generated by our second decoder. Furthermore, we present an end-to-end multiscale transformative discriminative neural network (MTDN) to super-resolve unaligned LR face images of different resolutions in a unified framework. We propose a method that explicitly incorporates structural information of faces into the face super-resolution process by using a multi-task convolutional neural network (CNN). Our method not only uses low-level information (i.e. intensity similarity), but also middle-level information (i.e. face structure) to further explore spatial constraints of facial components from LR inputs images. We demonstrate that supplementing residual images or feature maps with additional facial attribute information can significantly reduce the ambiguity in face super-resolution. To explore this idea, we develop an attribute-embedded upsampling network. In this manner, our method is able to super-resolve LR faces by a large upscaling factor while reducing the uncertainty of one-to-many mappings remarkably. We further push the boundaries of hallucinating a tiny, non-frontal face image to understand how much of this is possible by leveraging the availability of large datasets and deep networks. To this end, we introduce a novel Transformative Adversarial Neural Network (TANN) to jointly frontalize very LR out-of-plane rotated face images (including profile views) and aggressively super-resolve them by 8X, regardless of their original poses and without using any 3D information. Besides recovering an HR face images from an LR version, this thesis also addresses the task of restoring realistic faces from stylized portrait images, which can also be regarded as face hallucination

Seeing death : portraiture in contemporary postmortem photography

This thesis focuses on the aesthetics of the photographic representation of the actual dead body in Elizabeth Heyert's The Travelers (2004), Pieter Hugo's The Bereaved (2005) and Walter Schels and Beate Lakotta's Life Before Death: Portraits of the Dying (2004). The use of portraiture in each of these artist's series is crucial as it suggests an interest in the 'subjectness' of the corpse. Katarzyna Majak's (2011) theory of socialization as an attempt to lessen the scandal of the corpse through representation is central throughout this thesis. Majak argues that for the viewer the corpse is a scandal, because it discomfortingly presents the transformation of a body from subject to object. For Majak, socialization is essentially the taming of the dead body, achieved by re-presenting the corpse as an individual. Socialization emphasizes the subject-ness of the deceased individual, rather than the object-ness of the corpse, of pure unadulterated matter. The use of portraiture in each of the above series socializes the corpse by presenting the individual identity of the deceased as a subject, in varying degrees. Death is approached through the recognizable conventions of portraiture itself, thereby to some extent taming or domesticating the corpse. This thesis expands on Majak's valuable theory by establishing a continuum of socialization from subject-ness to object-ness. Importantly, this continuum reveals varying degrees of socialization within the three series. Socialization is used here as an analytical tool with which to explore the photographs, drawing out similarities and differences. I argue that through various aesthetic techniques, these three series encourage the viewer to look at these different images of the corpse with varying degrees of comfort

In the Vernacular: Photography of the Everyday

This is the catalogue of the exhibition "In the Vernacular" at Boston University Art Gallery