Computer algorithms are used by face recognition systems to identify certain recognizable features on a person's face. The information is then transformed into a mathematical representation and contrasted with information on other faces gathered in a face recognition database. Examples of these features are the space between the eyes or the contour of the chin. Face-recognition technology is quickly developing and used in various fields, including marketing, education, criminal investigation, security, and biometrics. Now, in addition to being able to identify the individual, it can also determine their facial expression.
Modern Virtual Reality applications require technology that supports photo-realistic human face rendering and restoration. Due to the social nature of people and their ability to read and convey emotions from minor changes in facial expressions, minute artifacts can cause the uncanny valley, which can be detrimental to the user experience. In order to solve challenging issues like innovative view synthesis and view-dependent effects modeling, several contemporary 3D telepresence techniques now make use of deep learning models and neural rendering. These methods are typically data hungry, and the efficiency of those models is directly influenced by the architecture of the capturing device and data pipeline. In order to push the envelope in such photo-realistic human face models, a sizable dataset of high-quality, multi-view facial photos encompassing a wide range of expressions is necessary. Such a dataset was presented by Meta researchers in recent work.
Face recognition has long been an active research area in the field of artificial intelligence, particularly since the rise of deep learning in recent years. In some practical situations, each identity has only a single sample available for training. Face recognition under this situation is referred to as single sample face recognition and poses significant challenges to the effective training of deep models. Therefore, in recent years, researchers have attempted to unleash more potential of deep learning and improve the model recognition performance in the single sample situation. While several comprehensive surveys have been conducted on traditional single sample face recognition approaches, emerging deep learning based methods are rarely involved in these reviews.
DeepFace is one of the most popular open source for Facial Recognition Library. Facial recognition has been a hot topic for several decades. And while there are different facial recognition libraries available, DeepFace has become widely popular and is used in numerous face recognition applications. DeepFace is the most lightweight face recognition and facial attribute analysis library for Python. The open-sourced DeepFace library includes all leading-edge AI models for face recognition and automatically handles all procedures for facial recognition in the background.
Researchers at Cornell University have developed an earphone that uses sonar to detect the wearer's facial expression to create an avatar of their face. The so-called "earable" system is called EarIO. It works by bouncing sound off the wearer's cheeks -- the audio is emitted from speakers on each side of the earphone. A microphone captures the echoes, which change as the face moves and the wearer speaks. The system then uses a deep learning algorithm to turn the echoes into a replica of the person's expression.
This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch.
Anti-surveillance makeup, used by people who do not want to be identified to fool facial recognition systems, is bold and striking, not exactly the stuff of cloak and daggers. While experts' opinions vary on the makeup's effectiveness to avoid detection, they agree that its use is not yet widespread. Anti-surveillance makeup relies heavily on machine learning and deep learning models to "break up the symmetry of a typical human face" with highly contrasted markings, says John Magee, an associate computer science professor at Clark University in Worcester, MA, who specializes in computer vision research. However, Magee adds that "If you go out [wearing] that makeup, you're going to draw attention to yourself." The effectiveness of anti-surveillance makeup has been debated because of racial justice protesters who do not want to be tracked, Magee notes.
A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks. A deep neural network that was trained to identify celebrity faces can be used to model primate face recognition, and reveals the importance of identity-selective neural units in face recognition.
Each passing day, the technologies surrounding AI-generated ultra-realistic faces become better and better. According to the artist, the project is a test of a Maya viewport applied in a real-time deepfake. To create the deepfake itself, the author used DeepFace Live. The workflow was based on that of Brielle Garcia, an AR/VR Software Developer, who is also known for creating realistic deepfakes.