Recent advance in machine learning has made face recognition not a difficult problem. But in the previous, researchers have made various attempts and developed various skills to make computer capable of identifying people. One of the early attempt with moderate success is eigenface, which is based on linear algebra techniques. In this tutorial, we will see how we can build a primitive face recognition system with some simple linear algebra technique such as principal component analysis. Face Recognition using Principal Component Analysis Photo by Rach Teo, some rights reserved.

Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create media that may cause social, political or economical upheaval. One emerging media is "Deepfake". Techniques that can discriminate between such media is indispensable. In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data. We test our approach Figure 1: Deepfake technique replaces a person's appearance in by representing Deepfakes with our modified multilinear (tensor) an existing image or video with someone else's appearance [20].

Previously, facial recognition technology was reserved for the movies and was a thing of fiction. However, much like other biometric solutions that have seen improvement and progress, facial recognition technology also steadily became a reality. Over the past decade, it has not only been developed and perfected; it is being deployed around the world as well. However, not as rapidly as other biometric technologies did – which include fingerprint, iris recognition, hand geometry, and DNA. Before we discuss the history and gradual evolution of facial recognition technology, there is a need to have an understanding of how this technology works and why there was a need for it in the first place?

During your time on earth you've seen hundreds of thousands of faces. Out of all of those faces, you've most likely recognized many of them. The phrase "I'd know that face anywhere" is very relevant here. Have you ever thought about how you recognize a face? The process is much more complicated than you'd think.

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.

Researchers from the Kuhl Lab at the University of Oregon explored how faces could be decoded from neural activity in the study Reconstructing Perceived and Retrieved Faces from Activity Patterns in Lateral Parietal Cortex, published in the Journal of Neuroscience. Hongmi Lee and Brice A. Kuhl tested whether faces could be reconstructed from the'angular gyrus' (ANG) located in the upper back area of the brain through functional magnetic resonance imaging (fMRI) activity patterns. They conducted the experiment by making facial reconstructions based on brainwave patterns from participants, initially during their perception of faces and later just from memory. Participants were shown more than 1,000 color photos of different faces, one after another, while an fMRI scan recorded their neural responses. The researchers then applied principal component analysis (PCA) to generate 300 'eigenfaces' - a set of vectors used in human face recognition.

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.

Face recall is a basic human cognitive process performed routinely, e.g., when meeting someone and determining if we have met that person before. Assisting a subject during face recall by suggesting candidate faces can be challenging. One of the reasons is that the search space - the face space - is quite large and lacks structure. A commercial application of face recall is facial composite systems - such as Identikit, PhotoFIT, and CD-FIT - where a witness searches for an image of a face that resembles his memory of a particular offender. The inherent uncertainty and cost in the evaluation of the objective function, the large size and lack of structure of the search space, and the unavailability of the gradient concept makes this problem inappropriate for traditional optimization methods. In this paper we propose a novel evolutionary approach for searching the face space that can be used as a facial composite system. The approach is inspired by methods of Bayesian optimization and differs from other applications in the use of the skew-normal distribution as its acquisition function. This choice of acquisition function provides greater granularity, with regularized, conservative, and realistic results.

Creation of summaries of events of interest from multitude of unstructured data is a challenging task commonly faced by intelligence analysts while seeking increased situational awareness. This paper proposes a framework called Storyboarding that leverages unstructured text and images to explain events as sets of sub-events. The framework first generates a textual context for each human face detected from images and then builds a chain of coherent documents where two consecutive documents of the chain contain a common theme as well as a context. Storyboarding helps analysts quickly narrow down large number of possibilities to a few significant ones for further investigation. Empirical studies on Wikipedia documents, images and news articles show that Storyboarding is able to provide deeper insights on events of interests.

Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modeled by low dimensional linear spaces.