Admiring the many faces of Facial Recognition with Deep Learning

How has deep learning modernised facial recognition

Traditional facial recognition algorithms would recognize images and people using distinct facial features (placement of eye, eye color, nose shape etc.) However, they failed in correct identification in cases of different lighting or slight change in the appearance ( beard growth, aging, or pose). In order to develop facial recognition techniques for a dynamic and ever-changing face, deep learning is proving to be a game changer. Deep Neural nets go beyond the approach of manual extraction. These AI based Neural Networks rely on image pixels to analyze features of a particular face. So they scan faces irrespective of the lighting, ageing, pose, or emotions. Deep learning algorithms remember each time they recognize or fail to recognize a problem. Thus, avoiding repeat mistakes and getting better at each attempt. Deep learning algorithms can also be helpful in converting 2D images to 3D.

Facial recognition in practice:

Facial Recognition Technology in Multimedia

Deep learning enabled facial recognition technologies can be used to track audience reaction and measure different levels of emotions. Essentially it can predict how a member of the audience will react to the remaining film. Not only this, it also helps determine what percentage of users will be interested in a particular movie genre. For example, Microsoft’s Azure Emotion,  an emotion API detects emotions by analysing the facial expressions on an image or video content over time. Caltech and Disney have collaborated to develop a neural network which can track facial expressions. Their deep learning based Factorised Variational Autoencoders (FVAEs) analyze facial expressions of audience for about 10 minutes and then predict how their reaction will be for the rest of the film. These techniques help in estimating whether the viewers are giving the expected reactions at the right place. For example, the viewer is not expected to yawn on a comical scene. With this, Disney can also predict the earning potential of a particular movie. It can generate insights that may help producers create compelling movie trailers to maximize the number of footfalls.

Smart TVs are also equipped with sophisticated cameras and deep learning algos for facial recognition ability. They can recognize the face of the person watching and automatically show channels and web applications programmed as their favorites. The British broadcasting corporation uses the facial recognition technology, built by CrowdEmotion. By tracking faces of almost 4,500 audience members watching show trailers, they gauge exact customer emotions about a particular programme. This in turn helps them generate insights to showcase successful commercials.

Biometrics in Smartphones

A large number of smartphones nowadays are instilled with biometric capabilities. Facial recognition in smartphones are not only used as a means of unlocking and authorizing, but also for making secure transactions and payments. In present times, there has been a rise in chips with built-in deep learning ability. These chips are embedded into smartphones. By having a neural net embedded inside the device, crucial face biometric data never leaves the device or sent to the cloud. This in turn improves privacy and reduces latency. Some of the real-world examples include Intel’s Nervana Neural Network Processor, Google’s TPU, Microsoft’s FPGA, and Nvidia’s Tesla V100.
Deep learning models, embedded in a smartphone, can construct a mathematical model of the face which is then stored in the database. Using this mathematical face model, smartphones can easily recognize users even as their face ages or when it is obstructed by wearable accessories.

Apple has recently launched the iPhone X facial recognition system termed as FaceID. It maps thousands of points on a user’s face using a projector and an infrared camera (which can operate under varied lighting conditions). This map is then passed to a bionic chip embedded in the smart phone. The chip has a neural network which constructs a mathematical model of the user’s face, used for biometric face verification and recognition. Windows Hello is also a facial recognition technology to unlock Windows smart devices equipped with infrared cameras. Qualcomm, a mobile technology organization, is working on a new depth-perception technology. It will include an image signal processor and high-resolution 3D depth-sensing cameras for facial recognition.

Face recognition for Travel

Facial recognition technologies can smoothen the departure process for a customer by eliminating the need for a boarding pass. A traveller is scanned by cameras installed at various check points, so they don’t have to produce a boarding pass at every step. Emirates is collaborating with Dubai Customs, Police and Airports to use a facial recognition technology solution integrated with the UAE Wallet app. The project is known as Together Initiative, it allows travellers to register and store their biometric facial data at several kiosks placed at the check-in area. This facility helps passengers to avoid presenting their physical documents at every touchpoint.

Face recognition can also be used for determining illegal immigration. The technology compares the photos of passengers taken immediately before boarding, with the photos provided in their visa application. Biometric Exit, is an initiative by US government, which uses facial recognition to identify individuals leaving the country.

Facial recognition technology can also be used at train stations to reduce the waiting time for  buying a train ticket or going through other security barriers. Bristol Robotics Laboratory has developed a software which uses infrared cameras to identify passengers as they walk onto the train platform. They do not need to carry tickets.

Retail and shopping

In the area of retail, smart facial recognition technologies can be helpful in fast checkout by keeping a track of each customer as they shop across a store. This smart technology, can also use machine learning and analytics to find trends in the shopper’s purchasing behavior over time and devise personalized recommendations. Facial video analytics and deep learning algorithms can also identify loyal and VIP shoppers from the moving crowd, giving them a privileged VIP experience. Thus, enabling them with more reasons to come back and make repeat purchases. Facial biometrics can also accumulate rich statistics about demographics(age, gender, shopping history) of an individual. Analyzing these statistics can generate insights, which helps organizations develop their products and marketing strategies.

FindFace is one such platform that uses sophisticated deep learning technologies to generate meaningful data about the shopper. Its e-facial recognition system can verify faces with almost 99% accuracy. It can also help route the shopper data to a salesperson’s notice for personalized assistance.

Facial recognition technology can also be used to make secure payment transactions simply by analysing a person’s face. AliBaba has set up a Smile to Pay face recognition system in KFC's. This system allows customers to make secure payments by merely scanning their face.

Facial recognition has emerged as a hot topic of interest and is poised to grow. On the flip side, organizations deploying such technology should incorporate privacy policies as a standard measure. Data collected from such facial recognition software can also be used wrongly for targeting customers with ads, or for other illegal purposes. They should implement a methodical and systematic approach for using facial recognition for the benefit of their customers. This will not only help businesses generate a new source of revenue, but will also usher in a new era of judicial automation.