The widespread integration of face recognition technologies into various applications (e.g., access control and personalized advertising) necessitates a critical emphasis on fairness. While previous efforts have focused on demographic fairness, the fairness of individual biological face components remains unexplored. In this paper, we focus on face component fairness, a fairness notion defined by biological face features. To our best knowledge, our work is the first work to mitigate bias of face attribute prediction at the biological feature level. In this work, we identify two key challenges in optimizing face component fairness: attribute label scarcity and attribute inter-dependencies, both of which limit the effectiveness of bias mitigation from previous approaches. To address these issues, we propose \textbf{B}ayesian \textbf{N}etwork-informed \textbf{M}eta \textbf{R}eweighting (BNMR), which incorporates a Bayesian Network calibrator to guide an adaptive meta-learning-based sample reweighting process. During the training process of our approach, the Bayesian Network calibrator dynamically tracks model bias and encodes prior probabilities for face component attributes to overcome the above challenges. To demonstrate the efficacy of our approach, we conduct extensive experiments on a large-scale real-world human face dataset. Our results show that BNMR is able to consistently outperform recent face bias mitigation baselines. Moreover, our results suggest a positive impact of face component fairness on the commonly considered demographic fairness (e.g., \textit{gender}). Our findings pave the way for new research avenues on face component fairness, suggesting that face component fairness could serve as a potential surrogate objective for demographic fairness. The code for our work is publicly available~\footnote{https://github.com/yliuaa/BNMR-FairCompFace.git}.

Early methods used face representations in kinship verification, which are less accurate than joint representations of parents' and children's facial images learned from scratch. We propose an approach featuring graph neural network concepts to utilize face representations and have comparable results to joint representation algorithms. Moreover, we designed the structure of the classification module and introduced a new combination of losses to engage the center loss gradually in training our network. Additionally, we conducted experiments on KinFaceW-I and II, demonstrating the effectiveness of our approach. We achieved the best result on KinFaceW-II, an average improvement of nearly 1.6 for all kinship types, and we were near the best on KinFaceW-I. The code is available at https://github.com/ali-nazari/Kinship-Verification

Google Brain's latest software can create sharpen images from a pixelated source. The system combines two neural networks and machine learning to guess what details lay hidden in the blurry picture. Once the system is fed an 8 x 8 pixelated image, the networks search for high-resolution images that it believes matches the source's content - and adds the missing details. The system combines two neural networks and machine learning to guess what details lay hidden in the blurry picture. Once the system is fed an 8 x 8 pixelated image, the networks search for high-resolution images that it believes matches the source's content The team at Google Brain has developed a system that is capable of making out details of a pixelated source.

Trying to picture yourself older or with a different hairstyle is near impossible. But now researchers have developed the ultimate face swap that analyzes a picture of your face, searches for images using key terms and seamlessly maps your it onto the results. Called Dreambit, this AI lets anyone see what they would look like with a different hairstyle or colour, or in a different time period, age, country or anything that can be queried in an image search engine. Dreambit lets anyone see what they would look like with a different hairstyle or colour, or in a different time period, age, country or anything that can be queried in an image search engine - as it has done with American actor George Clooney (pictured). 'Dreambit is a personalized image search engine,' reads the website.

Until we open our mouths to speak, it is possible for most of us to keep our thoughts to ourselves. But computers could soon be able to predict what you are thinking by looking for distinct patterns of activity in your brain that relate to sentences. Researchers have developed a computer program that is able to search for the brain activity related to certain words and then use this to predict a sentence being thought even it hasn't seen it before. Scientists have created a computer model that can predict unspoken sentences by looking at the neural activity in the brain. They say the system is able to get the predictions right around 70 per cent of the time.

Until we open our mouths to speak, it is possible for most of us to keep our thoughts to ourselves. But computers could soon be able to predict what you are thinking by looking for distinct patterns of activity in your brain that relate to sentences. Researchers have developed a computer program that is able to search for the brain activity related to certain words and then use this to predict a sentence being thought even it hasn't seen it before. Scientists have created a computer model that can predict unspoken sentences by looking at the neural activity in the brain. They say the system is able to get the predictions right around 70 per cent of the time.

Reading minds is an ability only found in comic book heroes. But new researcher has revealed that computer can now analyse brain scans and work out who a person is thinking about. The AI system can even create a digital portrait of the face in question. Researchers have reconstructed a face after peering into the mind of another by extracting latent face components from neural activity and using machine learning to create digital portraits. Researchers used an innovative form of fMRI pattern analysis to test whether lateral parietal cortex actively represents the contents of memory.