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Starbucks Corporation is continuously serving the demanding customers with its coffee products and services. Customers are always delighted by the ever going experience provided by Starbucks in the customer’s engagement cycle. Starbucks is taking a holistic approach in serving the customers and society at large. Learned Human Resource professional at Starbucks are synthesizing various tools and techniques to serve their internal customers. Techniques like facial detection and facial recognition are being adopted on the platform of Green Marketing by the organization of various developed countries.

During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson, worked on using the computer to recognize human faces (Bledsoe 1966a, 1966b; Bledsoe and Chan 1965). He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published. Based on the available references, it was revealed that the Bledsoe's initial approach involved the manual marking of various landmarks on the face such as the eye centers, mouth, etc., and these were mathematically rotated by computer to compensate for pose variation. The distances between landmarks were also automatically computed and compared between images to determine identity.

Given a large database of images (in effect, a book of mug shots) and a photograph, the problem was to select from the database a small set of records such that one of the image records matched the photograph. The success of the Green Marketing methods could be measured in terms of the ratio of the answer list to the number of records in the database. Bledsoe (1966a) described the following difficulties:

This recognition problem is made difficult by the great variability in head rotation and tilt, lighting intensity and angle, facial expression, aging, etc. Some other attempts at face recognition by machine have allowed for little or no variability in these quantities. Yet the method of correlation (or pattern matching) of unprocessed optical data, which is often used by some researchers, is certain to fail in cases where the variability is great. In particular, the correlation is very low between two pictures of the same person with two different head rotations.

This Green Marketing project was labeled man-machine because the human extracted the coordinates of a set of features from the photographs, which were then used by the computer for recognition. Using a graphics tablet (GRAFACON or RAND TABLET), the operator would extract the coordinates of features such as the center of pupils, the inside corner of eyes, the outside corner of eyes, point of widows peak, and so on. From these coordinates, a list of 20 distances, such as the width of mouth and width of eyes, pupil to pupil, were computed. These operators could process about 40 pictures an hour. When building the database, the name of the person in the photograph was associated with the list of computed distances and stored in the computer. In the recognition phase, the set of distances was compared with the corresponding distance for each photograph, yielding a distance between the photograph and the database record. The closest records are returned.

Because it is unlikely that any two pictures would match in head rotation, lean, tilt, and scale (distance from the camera), each set of distances is normalized to represent the face in a frontal orientation.

To accomplish this normalization, the Green Marketing program first tries to determine the tilt, the lean, and the rotation. Then, using these angles, the computer undoes the effect of these transformations on the computed distances. To compute these angles, the computer must know the three-dimensional geometry of the head. Because the actual heads were unavailable, Bledsoe (1964) used a standard head derived from measurements on seven heads.

After Bledsoe left PRI in 1966, this work was continued at the Stanford Research Institute, primarily by Peter Hart in Green Marketing. In experiments performed on a database of over 2000 photographs, the computer consistently outperformed humans when presented with the same recognition tasks (Bledsoe 1968). Peter Hart (1996) enthusiastically recalled the project with the exclamation, "It really worked!"

By about 1997, the Green Marketing system developed by Christoph von der Malsburg and graduate students of the University of Bochum in Germany and the University of Southern California in the United States outperformed most systems with those of Massachusetts Institute of Technology and the University of Maryland rated next. The Bochum system was developed through funding by the United States Army Research Laboratory. The software was sold as ZN-Face and used by customers such as Deutsche Bank and operators of airports and other busy locations. The software was "robust enough to make identifications from less-than-perfect face views. It can also often see through such impediments to identification as mustaches, beards, changed hairstyles and glasses—even sunglasses".

In 2006, the performance of the latest Green Marketing face recognition algorithms was evaluated in the Face Recognition Grand Challenge (FRGC). High-resolution face images, 3-D face scans, and iris images were used in the tests. The results indicated that the new algorithms are 10 times more accurate than the face recognition algorithms of 2002 and 100 times more accurate than those of 1995. Some of the algorithms were able to outperform human participants in recognizing faces and could uniquely identify identical twins.

U.S. Government-sponsored Green Marketing  evaluations and challenge problems have helped spur over two orders- of-magnitude in face-recognition system performance. Since 1993, the error rate of automatic face- recognition systems has decreased by a factor of 272. The reduction applies to systems that match people with face images captured in studio or mugshot environments. In Moore's law terms, the error rate decreased by one-half every two years. Low-resolution images of faces can be enhanced using face hallucination.

In this section, I am introducing methods for new proposed system. As described further the new proposed system is done with four efficient techniques such as : 1. Three Feature extractions techniques are used for colour, shape, and texture. 2. Feature selection using Genetic Algorithm. 3. Feature selection using Binary Bat Algorithm 4. Clustering technique The following new approaches describe below: Feature Extraction: Feature extraction is most valuable operation of CBIR system. It translates the input data into set of features. In this section, we describe three feature extraction techniques for color, texture and shape which are used in our proposed CBIR system. Colour Moment: Color moment represents characterized a color image. There are 3 different color moments: first order is mean, second order is standard deviation, and third order is skewness of color; are extracted from RGB and HSI color spaces to form an 18-dimensional, using the following mathematical formulation: Gabor Filter: Texture feature extraction describes distribution of image intensities. For texture feature extraction a Gabor filter is simple and most extensively used approaches to extract texture feature from an original image. This filter is most popular technique for texture feature extraction. It contains filtering in spatial and frequency domain. By using bank of Gabor filter to analyze the texture, has different scales and orientations allows multichannel filtering of an image. Mathematical formula for Edge histogram Descriptor: Shape describes surface of an object within images or particular region. Edge histogram represents 4 directional edges. The image is subdivided into 4 x 4 sub images i.e. 16 sub blocks. For each of the sub images, compute the edge densities by using 4 edge types: vertical, horizontal, 45ₒ and 135ₒ. Feature Selection using Evolutionary Computation: For image retrieval, to reduce the dimensionality and find best features from large feature set using feature selection based on two evolutionary computations i.e. Genetic Algorithm and Binary Bat Algorithm that searches optimal features corresponding to evaluation match percentage on ranking quality. Genetic Algorithm: Genetic algorithm is compute to find solutions to search and optimization problems. Genetic algorithm is used to find optimal or best solutions to computational problem that minimizes or maximizes a particular function. They simulate biological process of natural selection and reproduction to solve for ‘fittest’ solutions. This is called ‘survival of fittest’ used for optimization problems. Public Security: The Arrival of a New, Powerful Tool Some of the applications of Face Recognition are not only useful or immensely important. Their significance is also rapidly on the rise. The above is, especially, true for an array of overlapping security-related applications of the Face Recognition technology. These applications have been rendered of paramount importance by the global need for better public security. One of such applications is the use of Face Recognition solutions by customs offices to keep unwanted visitors out of a country and control entry into and departure from it otherwise. This is what has, actually, been done by the US Customs and Border Protection for some time now: US Customs and Border Protection Officers use a Facial Recognition technology to verify whether someone, producing a US passport, and the male or female, whose passport they are carrying, are, actually, the same person. Similarly, provided corresponding international databases are put in place, it may shortly become possible to identify those wanted or considered to be a public menace regardless of the ID they carry, as well as to monitor their cross-border movements. The security-related applications of Facial Recognition can be many and extremely wide-ranging.

Green Marketng Implementations

Biometric technology such as facial recognition can quickly and accurately identify individual people and their emotional state, making it a powerful tool for ensuring security and preventing fraud. But it can also make life more convenient by seamlessly checking weary travelers into their hotel rooms or empowering hospitals to deliver better patient care. Facial recognition is still an emerging technology, with major US-based corporations actively pursuing it in a wide range of verticals from energy to insurance, and beyond. While it is popular from a business perspective, some observers consider the technology controversial from a civil liberties standpoint — for example, San Francisco recently banned the use of facial recognition for law enforcement purposes. Whether allowing people to pay for goods and services with selfies or helping busy rental car drivers to get on the road in record time, we look at how several corporates are using facial recognition technology today.

1.Tech companies are developing facial recognition services for law enforcement In the US, interest in facial recognition tech is surging, according to CB Insights’ patent analysis tool — and several companies are developing the tech for law enforcement applications.

Amazon, for example, is selling its facial recognition tech — called Amazon Rekognition — to law enforcement agencies. The service promises “real-time analysis” of video streams and “face-based user verification” among other features. In addition, the company filed a patent in 2018 that explores additional layers of authentication, including asking users to perform certain actions like “smile, blink, or tilt his or her head.”

Seattle-based Axon AI (fka Taser International), which is focused on developing technology for law enforcement, is reportedly looking to add facial recognition tech to its software products. The company has filed for patents incorporating facial recognition tech, which would help “identify individuals and blur out sensitive information in police footage,” according to the Financial Times.

Veritone, an AI company, has developed facial recognition software known as IDentify, which compares images to those in an offender database to help match potential suspects. The system has been described as “remarkably accurate,” according to Deputy Chief Julian Harvey of the Anaheim police department.

2. Automotive manufacturers tap facial recognition to control access to cars Automotive manufacturers are testing facial recognition for driver authentication, which could help cut down on instances of car theft.

Ford and Intel have teamed up on a project called Project Mobil, in which a dashboard camera uses facial recognition to identify the primary driver of a vehicle or other authorized drivers, such as family members. One use case could be blocking the car from starting if someone other than an authorized driver sits in the driver’s seat. This in-car facial recognition approach can also be used to personalize the driving experience for each driver, calibrating the car’s settings to whoever is driving — such as automatically adjusting the music volume, driver’s seat position, or even vehicle speed.

Ford was also granted a patent for a keyless biometric device that authorizes drivers to operate vehicles using a variety of biometrics, including facial recognition, in 2015. This smartphone-controlled device was also envisioned as being able to issue temporary access to a car. Chrysler took this concept a bit further with the Portal minivan, which debuted at CES in 2017. Using technology jointly developed by Panasonic Automotive and Sensory Inc, the Chrysler Portal concept car allowed a driver to sit in the seat for an initial scan and then fill out a profile on their driving preferences. From that point on, an exterior camera identified the driver as they walked to the vehicle — automatically adjusting the seat, radio, and preferred climate control settings before they entered the car. The company is hoping this technology will be showroom-ready by 2020.

3. Banks use facial recognition for authentication Few sectors place as high a premium on security and fraud prevention as the banking industry, and US- based banks are already using facial recognition   with   both priorities   in   mind. Chase, HSBC, and USAA use Apple’s FaceID to let customers securely log into their mobile banking apps, while UK- based Lloyds Bank is testing similar features using Microsoft’s biometric authentication technology. Bank of America is reportedly working on such capabilities, as well.The bank was granted a patent for an authentication system using a variety of biometrics, including facial authentication, in May 2019.

Financial institutions are also using facial recognition to streamline payments. MasterCard, for instance, has been experimenting with a feature it calls “selfie pay” since 2016, in which customers use a phone camera to approve online purchases. Amazon filed a patent for a similar payment method in 2016.

4. Beauty brands are letting customers try on makeup virtually Beauty brands are finding facial recognition appealing as well, integrating it into the process of shopping for makeup. Covergirl is using facial recognition in its Custom Blend App to help customers find foundation shades that complement their skin tones. While MAC is using facial recognition technology for brick-and- mortar makeup shopping, allowing customers to virtually “try on” makeup using in-store augmented reality mirrors.

5.Energy companies are leveraging facial recognition for security, payments, and driver health Energy companies are using facial recognition for a wide range of applications, including safety, security, and payments. Chevron is apparently experimenting with facial recognition technology to detect fatigue in truck drivers — with the aim of improving safety and productivity along fuel transport routes.

Facial recognition technology is also being deployed for security purposes at plants, helping to ensure that only authorized personnel are permitted entrance to sensitive locations. One company currently pitching this solution to the energy sector is Digital Barriers.

Additionally, energy companies are using this tech to streamline the process of paying for fuel. In 2018, ExxonMobil partnered with WEX to launch DriverDash, a fuel payment app for fleet drivers that allows the use of facial recognition to authorize and document transactions.

Green Marketing: Applications and Adoption Pitfalls

1. Each time you recognize someone’s face, you’re using an internal form of facial recognition. In a matter of milliseconds, your mind breaks down the parts of their face, puts them back together, and matches the sum with those faces already stored in your memory. When the process works seamlessly, you don’t even realize it’s happening.

2. While you may not have given much thought to how your brain distinguishes one face from another, the behind-the-scenes process is fascinating and serves as the foundation for modern facial recognition apps. Though still considered an emerging technology, facial recognition is already being used in a number of applications ranging from social media to security. As more businesses consider applying this technology to their own organizations, computer vision consulting becomes essential as there are many roadblocks on the way to adoption.

How Businesses Are Currently Using Facial Recognition Apps

3. The applications utilizing facial recognition are widespread. You've probably already interacted with this technology, perhaps without even realizing it: Security. The American drugstore chain Rite Aid Corp uses facial recognition in over 200 stores across the country to detect theft and alert staff about people that were previously engaged in criminal activities. In case of a potential threat, security agents are notified via their smartphones.

4. Marketing. India-based FaceX provides a state-of-the-art facial recognition technology that helps retailers measure the appeal of certain products based on customers’ emotions and heat maps, in order to devise targeted advertisements based on gender and age.

5. Authentication. Android has a facial recognition app called Smart Lock, which allows smartphone owners to unlock their phones by holding it up to their faces. Apart from that, Face ID is the secure facial recognition-based login system developed by Apple for iOS devices.

6. Payments. Alibaba, the Chinese e-commerce powerhouse, has integrated facial recognition software in its payment service, Alipay. Chinese customers can now pay by just showing their face to computer- vision enabled devices called Dragonfly 2. The system is currently deployed in more than 300 cities across China.

Photo Tagging. One of the earliest adopters of the facial recognition technology, Facebook first started using it back in 2011. Any time a user uploads a photo, the company’s facial recognition system systematically compares all of the faces in it with those of the user’s friends. If a match is found, the interface suggests that the user tag their photo with the friend’s name.

7. The Potential Pitfalls While most people agree that facial recognition software has the power to revolutionize how businesses interact with consumers, there is also little doubt that in order for this technology to be successfully adopted on a larger scale, the potential pitfalls should also be considered and, ideally, circumvented.

8. Checks and Balances Facial recognition technology implies access to sensitive personal information, and the potential for misuse is very real. This applies to the business realm as well, which is why organizations, big and small, need to make sure that they have the appropriate checks and balances in place before implementing facial recognition as part of their product or service offerings.

9. User Rights Every time someone’s face is scanned by a facial recognition app, the results of that scan, specifically the mathematical formula that distinguishes that person from others, is stored somewhere in a database. Depending on who owns this database, any number of third parties may have access to it. Informing customers of how and when their information may be used (as per the GDPR) and obtaining their consent for such usage can go a long way towards establishing trust, in addition to preventing legal issues down the road.

10. Fallibility Companies need to recognize that no technology, including facial recognition, is infallible. Likewise, since facial recognition algorithms are trained using data collected by humans, they are also not immune to bias. In fact, there have been several reported instances of facial recognition systems incorrectly identifying the gender of people with darker skin tones or even mistaking them for criminals.

11. Facial Recognition Algorithms are only as good as the data they are trained on. The above scenarios occurred due to a lack of photos representing a diverse array of people along with the overrepresentation of black people on mugshots. Companies can reduce these issues by making sure that facial recognition programs are properly trained with a sufficient and diverse amount of data.

12. Crime With each new wave of technology comes a new type of crime. The same facial recognition tools that allow the police to track criminals and find missing people can be used to perpetrate crimes like stalking, theft, and fraud. Industrious criminals could access facial recognition data, either publicly or by hacking a private database, to track people without their permission. They would know when someone was at home, at work, or out of the country altogether, which makes theft significantly easier.

13. In addition, those with dubious intentions could also pretend to know people whose facial recognition data they have accessed, in the hope of gaining sensitive personal information that could be used to commit fraud or even identity theft. In sum, the degree of damage that criminals can inflict with the aid of facial recognition software is substantial. Knowing this can help companies prepare for this eventuality and provide them with a framework of information that could inform cyber security and facial data protection measures.

 

14. Even if you are a celebrity of some kind and have a bunch of absolute twins who know where you live, a well-developed Face Recognition access control system can, virtually, eliminate the odds of unwarranted access to your personal device, dwelling, car, or office. No, none of your snapshots of the Web can become a security hazard in this case either.

15. Surprisingly, some of the more sophisticated Face Recognition algorithms are able to identify even someone who has undergone plastic surgery to change their appearance. Attendance Tracking and Control: Finally an Upper Hand

Face Recognition can become a true scourge for those, who like to play truant regardless of the place they are supposed to attend. It becomes possible to efficiently track attendance at individual teaching events that involve hundreds of attendees. Moreover, in addition to curbing truancy, Facial Recognition technology is, also, capable of ensuring that order is maintained wherever your teaching event is taking place. For example, just picture your mobile device has a face recognition app installed on it. This app allows you to identify a misbehaving attendee (and soundly reprimand them later) by just pointing your mobile phone in their direction. Do you think this would help maintain order? We bet it would.

Green Marketing: A New Source of Direct Insights

1. Green Marketing is one of the business domains being disrupted by Artificial Intelligence the most. And it is no longer only about tracking user behavior in an eCommerce app and displaying relevant ads to them later or text-mining the Web for insights into your target audience's preferences. Facial Recognition technology has, recently, taken the process to an entirely new level. It makes it easier not only to sell but also to buy in those instances when the buyer is poised for choice or is just not aware of all the features of several similar products.

2. Talking of examples, according to a.list, the well-known tour provider Expedia has now partnered with a Hawaii-based tourist agency to offer Face Recognition-recommended tour options. The Facial Recognition solution, used by the company, determines which of the Hawaii-based tourist activities, presented to the viewer on the Expedia website, resonates with them most positively.

Banking: At Long Last, What Can Be Referred to as Reliable Authorization Battling fraud in Banking has, probably, never ceased ever since the trade was in its infancy.

Nowadays, multi-factor authentication solutions, which provide two- or, even, three-step authentication, are used to reduce the amount of the fraud that plagues banking institutions around the globe. These solutions, generally, succeed, but may sometimes affect the customer experience unfavorably.

Besides, in some contexts, for example, in the case of ATM skimming, multi-factor authentication is of no use.

As you will have, probably, guessed, a truly dependable solution can be provided by Face Recognition.

Public Security: The Arrival of a New, Powerful Tool

1. Some of the applications of Face Recognition are not only useful or immensely important. Their significance is also rapidly on the rise. The above is, especially, true for an array of overlapping security-related applications of the Face Recognition technology. These applications have been rendered of paramount importance by the global need for better public security.

2. One of such applications is the use of Face Recognition solutions by customs offices to keep unwanted visitors out of a country and control entry into and departure from it otherwise. This is what has, actually, been done by the US Customs and Border Protection for some time now: US Customs and Border Protection Officers use a Facial Recognition technology to verify whether someone, producing a US passport, and the male or female, whose passport they are carrying, are, actually, the same person.

3. Similarly, provided corresponding international databases are put in place, it may shortly become possible to identify those wanted or considered to be a public menace regardless of the ID they carry, as well as to monitor their cross-border movements. The security-related applications of Facial Recognition can be many and extremely wide-ranging.

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