Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib. Experiments show that human beings have 97.53% accuracy on facial recognition tasks whereas those models already reached and passed that accuracy level. The easiest way to install deepface is to download it from PyPI. It's going to install the library itself and its prerequisites as well.

Parents do discuss whom their child is looking like but no one can really be convinced about the result with this discussion. Luckily, we have very powerful facial recognition technology nowadays to learn the real and unbiased answer. In this post, we are going to use deepface to decide a child looking more like to which parent. We normally use facial recognition technology to verify face pairs are same person or different persons. Face pairs are represented as multi-dimensional vectors by facial recognition models such as FaceNet.

This article corresponds to the class notes about Face Recognition taken by me on the Convolutional Neural Networks Andre Ng's 4th course of the Deep Learning Specialization of DeepLearning.ai. Hope you find this material helpful. In the Face recognition literature, people often talk about face verification and recognition. How could we define these problems? Given an input image as well as name or ID, and the job of the system is to verify whether or not the input image is that of the claimed person, also called a one to one problem.

Brain "plasticity" is one of the great discoveries in modern science, but neuroscientist David Eagleman thinks the word is misleading. Unlike plastic, which molds and then retains a particular shape, the brain's physical structure is continually in flux. But Eagleman can't avoid the word. "The whole literature uses that term plasticity, so I use it sparingly," he says. Eagleman also discounts computer analogies to the brain. He's coined the term "livewired" (the title of his new book) to point out that the brain's hardware and software are practically inseparable. Eagleman is a man of prodigious energy. An adjunct professor at Stanford University, he's also been a novelist, TV host of PBS's The Brain, and science advisor for the HBO series Westworld.

Facial recognition may not be as secure as previously thought. Researchers found that the technology can be fooled by using a 3D-printed mask depicting a different person's face. The mask was able to trick payment a system at a border checkpoint in China a passport-control gate in Amsterdam. The security flaw was discovered by researchers with the artificial intelligence firm Kneron, which determined criminals only need is a lifelike mask of a person to bypass security checkpoints. Kneron CEO Albert Liu said in a statement: 'Technology providers should be held accountable if they do not safeguard users to the highest standards.'

Face Verification checks "is this the claimed person?". For example, in school, you go with your ID card and the invigilator verifies your face with the ID card. A mobile phone that unlocks using our face is also using face verification. It is 1:1 matching problem. Now suppose the invigilator knows everyone by their name.

We present a straightforward non-iterative method for shallowing of deep Convolutional Neural Network (CNN) by combination of several layers of CNNs with Advanced Supervised Principal Component Analysis (ASPCA) of their outputs. We tested this new method on a practically important case of'friend-or-foe' face recognition. This is the backyard dog problem: the dog should (i) distinguish the members of the family from possible strangers and (ii) identify the members of the family. Our experiments revealed that the method is capable of drastically reducing the depth of deep learning CNNs, albeit at the cost of mild performance deterioration. 1. Introduction IT giants have produced many software "semiproducts" for image recognition. This new opportunity gave rise to many works in face recognition. These works and popular critics of their results prove that the performance of these systems are problem-depending and the devil is in the detail of testing and validation: the systems, which are almost perfect for one problem can be useless for another one. In this paper we focus on a problem which, on the one hand, appears to be a close relative of the face recognition applications and yet, on the other hand, is somewhat more relaxed.

A baseball cap that can fool facial recognition systems into think you're someone else has been developed by scientists. The face-stealing hat projects infrared light - which is invisible to the naked eye - onto your face to trick AI camera systems, which can see the spectrum. Researchers said the technology can not only obscure your identity but also'impersonate a different person to pass facial recognition-based authentication.' A baseball cap that can fool facial recognition systems into think you're someone else has been developed. They added that the face-stealing lights could easily be'hidden in an umbrella and possibly even hair or a wig.' Writing in the pre-publish journal ArXiv, the joint US and Chinese team, led by Dr Zhe Zhou of Fudan University in Shanghai, said: 'We propose a kind of brand new attack against face recognition systems, which is realised by illuminating the subject using infrared. 'Through launching this kind of attack, an attacker not only can dodge surveillance cameras.

One of the most discussed features of the new iPhone X is the new unlocking method, the successor of TouchID: FaceID. Having created a bezel-less phone, Apple had to develop a new method to unlock the phone in a easy and fast way. While some competitors continued using a fingerprint sensor, placed in a different position, Apple decided to innovate and revolutionize the way we unlock a phone: by simply looking at it. Thanks to an advanced (and remarkably small) front facing depth-camera, iPhone X in able to create a 3D map of the face of the user. In addition, a picture of the user's face is captured using an infrared camera, that is more robust to changes in light and color of the environment. Using deep learning, the smartphone is able to learn the user face in great detail, thus recognizing him\her every time the phone is picked up by its owner.

If you work in the tech sector or have interest in the tech scene, you've probably heard the term "deep learning" floating around quite a bit. It's the emerging area of computer science that is revolutionizing artificial intelligence, allowing us to build machines and systems of the future. Although deep learning is making our lives easier, understanding how it works can be hard. Having spent quite some time exploring the world of deep learning, mostly for computer vision applications, I learned a thing or two on what it's all about and therefore I'm here to share what I learned. Firstly, before you understand deep learning, it's important that you know what machine learning is.