Most dog owners will insist they can tell what their pooch is thinking from their face alone. But man's best friend used to be even more expressive, according to a new study. Researchers have discovered that the domestication process has resulted in the loss of some communication abilities in today's dogs compared to their wolf ancestors. The team, from Durham University, used a'Dog Facial Action Coding System' to analyse video recordings of captive wolves and domestic dogs. This was during both spontaneous social interactions and reactions to external stimuli, for example a squeaky toy.

Have you interacted with a digital persona yet? At the Museum of Art & Photography in Bangalore, you can have a deep and engaging exchange with one that represents the late artist M.F. Husain -- considered the "Picasso of India" by many. This avatar is eager to talk art. And if you ask him whether he's real, he will look straight at you and say, "As close to real, enough to impress you."

State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variations. In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. Our work is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes (e.g., identity, facial expression, texture, illumination, and face pose). Furthermore, the priors can easily be incorporated into any network and are extremely efficient in improving the performance and accelerating the convergence speed. Firstly, a 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge. Secondly, the Spatial Attention Module is used to better exploit this hierarchical information (i.e., intensity similarity, 3D facial structure, and identity content) for the super-resolution problem. Extensive experiments demonstrate that the proposed 3D priors achieve superior face super-resolution results over the state-of-the-arts.

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

Controversial AI software that researchers claimed could determine if someone is gay by looking at the shape of their face has been debunked. Experts say that the computer program, developed by Stanford University, is not able to determine your sexuality by scanning photos. Instead, they claim it relies on patterns in how homosexual and heterosexual people take selfies to make its determinations. That includes superficial details like the amount of makeup and facial hair on show, as well as different preferences for the type of angles used to take the shots. Critics slammed the software when it first emerged in September 2017, saying it could be used to'out' men and women currently in the closet.

A facial recognition system can identify someone even if their face is covered up. The Disguised Face Identification (DFI) system uses an AI network to map facial points and reveal the identity of people. It could eventually help to pick out criminals, protesters, or anyone who hides their identity by covering themselves with masks, scarves or sunglasses. The software could also see the end of public anonymity, sparking privacy concerns from one academic, who has labelled it'authoritarian'. A facial recognition system can identify someone even if their face is covered up.