AgeFlow: Conditional Age Progression and Regression with Normalizing Flows

Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively. Existing generative adversarial networks (GANs) based methods suffer from the following three major issues: 1) unstable training introducing strong ghost artifacts in the generated faces, 2) unpaired training leading to unexpected changes in facial attributes such as genders and races, and 3) non-bijective age mappings increasing the uncertainty in the face transformation. To overcome these issues, this paper proposes a novel framework, termed AgeFlow, to integrate the advantages of both flow-based models and GANs. The proposed AgeFlow contains three parts: an encoder that maps a given face to a latent space through an invertible neural network, a novel invertible conditional translation module (ICTM) that translates the source latent vector to target one, and a decoder that reconstructs the generated face from the target latent vector using the same encoder network; all parts are invertible achieving bijective age mappings. The novelties of ICTM are two-fold. First, we propose an attribute-aware knowledge distillation to learn the manipulation direction of age progression while keeping other unrelated attributes unchanged, alleviating unexpected changes in facial attributes. Second, we propose to use GANs in the latent space to ensure the learned latent vector indistinguishable from the real ones, which is much easier than traditional use of GANs in the image domain. Experimental results demonstrate superior performance over existing GANs-based methods on two benchmarked datasets. The source code is available at https://github.com/Hzzone/AgeFlow.

The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

Extending the “Reach” of the Short-Scar Facelift with Energy Devices—An Early Report

Facial rejuvenation has become more popular. A wider breadth of the patient population is seeking procedures to preserve their youthful facial attributes and to remedy age-related deleterious changes. Along with this increasing interest in facial rejuvenation is also the expressed desire for any interventions to be relatively low risk, with limited recovery, and with achievable positive results. Many new technologies have become available in an attempt to improve age-related facial changes. The radiofrequency (RF)-based technologies are largely directed toward skin tightening and toward reducing and remodeling subcutaneous fat. It can be contemplated that the combination of RF-based technology with limited surgical procedures may extend the patient selection for less invasive procedures while improving potential results. This is a report of the combination of radiofrequency technologies—percutaneous and transcutaneous—with short scar face techniques in facial rejuvenation.

A Genetic Algorithm to Combine Deep Features for the Aesthetic Assessment of Images Containing Faces

The automatic assessment of the aesthetic quality of a photo is a challenging and extensively studied problem. Most of the existing works focus on the aesthetic quality assessment of photos regardless of the depicted subject and mainly use features extracted from the entire image. It has been observed that the performance of generic content aesthetic assessment methods significantly decreases when it comes to images depicting faces. This paper introduces a method for evaluating the aesthetic quality of images with faces by encoding both the properties of the entire image and specific aspects of the face. Three different convolutional neural networks are exploited to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e., low/high, and continuous aesthetic score prediction on four different image databases in the state-of-the-art.