Why Industries are adopting Face Recognition Technology as a game-changer

India is currently one of the fastest growing economies in the World. One of the hallmarks of success for the Indian Industry leaders have always been about how they cope up with a fast changing environment. In recent times, technology has been a key differentiator to determine who stays ahead in the race. To adapt to and proactively adopt newer technologies in the fast-changing technical landscape has become a key ingredient for success for the present-day Industry leaders.

Many of the new technologies and innovation have been horizontal in nature, meaning they have applications across multiple sectors and industries. Technologies like Internet, Bluetooth, IOT, 3D Printing etc have never been sector or a use-case specific, but brought revolutionary changes across various Industries that optimized existing businesses, addressed pain points that have not been addressed before and enabled industries to cater to various adjacent use-cases that remained unaddressed earlier.

Face recognition is a similar technology that has been horizontal in nature. Innovators in various industries have been trying to understand the fitment of this revolutionary image processing technology in their specific landscape. The questions they are pondering over are : Why Face Recognition Technology for them? How can it bring real impact?

Leaders tend to see new technology as magic wand to resolve multiple existing challenges. They ideate regarding the new doors that can open using new technology and use their domain expertise to understand what business problems the new technology can solve in their specific sector. Few of such examples would be like how Portable Face Biometric Technology has made a significant impact in eradicating subsidy leakage problem from various social welfare programs, as well as increasing the revenues of companies where on-field sales and marketing activities have a strong impact on the company’s balance sheet.

Leaders also identify what are the next best alternatives to the new Technology and then identify gaps in these available alternatives and explore to what extent new technology can plug those gaps. Verifying bank users and beneficiaries with their Facial imprints in existing databases like UIDAI (Aadhaar Cards), recognizing valuable customers when they walk into the Stores are few examples where existing alternatives were not able to address the gaps, which have been addressed by Portable Face Biometric Technology now.

Portable Face Biometric Technology is one of the most mature and reliable flavours of Face Recognition Technology and is available in the form of Smartphone based Face Recognition, Verification and Tracking system(SFRVTS). This Face recognition Technology identifies faces in images captured from any photo capturing devices like IP Camera, Tablet, WebCam, Mobile Phones etcIt handles effectively the cases of pose variations and lighting alterations during taking pictures, along with facial changes of an individual over a period of time.

We have attempted to compare this Smartphone based face recognition, verification and tracking solution (SFRVTS) on various parameters with competing technologies like Fingerprint scanner, USB based Fingerprint hardware, Iris scanner, first generation facial scanners, Log in & Log out based portals etc to understand comparatively the quantum of benefits it provides in various operational dynamics, various use cases along with features. Here is the comparison table:

For the Innovators & Decision Makers
Use case
Legends
For the problem solvers
Features
Hassles.png
While this is a small roundup of the Use-Cases and features of Face Recognition technology and similar technologies/options in action, we would love to hear from you if there have been any glaring omission.
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Year 2015 For Face Recognition: 10 -Best Innovations & Implementations

 

Research in recent face recognition technologies seem to suggest that it is challenging to fake the identity of the person even after a facial surgery (though, we find that hard to believe) because they make use of something called as ‘nodes’ (around 600 nodes for every face) of the facial structure, which these techniques claim, remains unique to each person – dead or alive.

Face recognition has its distinct advantage of being non-contact in nature compared to the other well-known bio metrics. A distant webcam/ video recorder and off lately smart phones, are capturing details required for bio metric systems that use Face Recognition, without any extra effort required by the people whose biometrics are being captured. Coming to think of it, this is in itself is a revolution in terms of the technological advancements that have taken place to enable such a seamless biometric operation to take place.

We have seen that face recognition technology backed products are becoming part of the mainstream. Here are a few use cases of face recognition technology being used to impact societies across different geographies:

Tweet: World's Top 10 Usage of Face Recognition Technology #2015 http://ctt.ec/qi076+

      1. New York & New Jersey, USA: New York and New Jersey are the first states using facial recognition software to bust illegal drivers at the Department of Motor Vehicles (DMV). CBS 2’s Jessica Schneider says it’s the key technological tool now used by law enforcement. It has already helped authorities make 3,500 arrests of the illegal or dual motor license fraudsters.
      2. Bangalore, India: LabourNet Services has adopted SmartAttendance, “a mobile based face recognition solution” to make sure the expected beneficiaries are receiving educational and skill development services at right time and place. As a social enterprise they are able to
        • Control the drop out rates of the enrolled trainees
        • Ensure the presence of expected trainees at the training sessions
        • Automate the complete monitoring process
      3. Arizona,USA:  The number of potential ID fraud cases identified has increased by 860 percent, as reported by the Arizona Republic. The Arizona Department of Transportation (ADOT) has implemented a commercial-off-the-shelf (COTS) vendor-hosted facial recognition solution, to support the Motor Vehicle Division (MVD) credential-issuance process.  This will further enhance fraud and identity theft protection for the citizens of Arizona and over time, expedite the processing and cut the number of fraud investigation cases. The system cross-references new driver’s license pictures with the MVD’s database of existing drivers.
      4. USA: Apple and Facebook have already worked on a mechanism that auto shares the images their social circles based on face recognition
      5. Indonesia, US, Portugal, Africa, India : Using face recognition to identify faces of church visitors from CCTV footage or photos to match churchgoers used to check attendance numbers, and alert church officials if some members stop coming to services. Attendance of these events and services is a key indicator on how the church performs in terms of popularity and growth. It also allows the church to keep a close connection to its members. Most churches already keep track of members attendance manually. However, when it comes to big events it is nearly an impossible task to track members.  It can also screen for people banned from the church.
      6. USA: Customs and Border Protection Officers stationed at air ports of entry are using facial recognition technology as a tool to help them in determining whether an individual presenting themselves with a valid U.S. electronic passport is the same individual photographed in that passport. The U.S. Customs and Border Protection (CBP) is conducting 1:1 Facial Recognition to identify this.
      7. China: A team of university and technology company researchers in China introduced an ATM with built-in facial recognition technology to cut the risk of illegal withdrawals.  The new ATM, built by a team from Tsinghua University and Tzekwan Technology, which captured the facial images using the cameras equipped in the ATM and later compare them with ID photos for verification. The facial recognition measure will be an added layer of identification on top of the traditional password or PIN required to access funds with a card. The ATM will also be linked to banks and local police offices to further bolster security.
      8. India: A social enterprise delivering health services in remote places (rural and semi rural areas) has adopted a novel solution to make sure beneficiaries receive the services at the right place and time through the on-the-field employees. The SmartAttendance, a mobile based face recognition and tracking solution collects bio metric data tagged with GPS and time stamp to detect and identify beneficiaries at meeting venues. This  has enabled the organization to track, monitor and audit the data coming from all the on-site service locations across the country. Salary calculations, productivity analysis and auditing are made extremely simple through the use of visual dashboards.
      9. Europe: Government and law enforcement agencies took a huge technological leap by providing their agents with the ability to identify individuals using facial recognition in ‘near’ real-time. Agents can now transmit photos or videos captured on their smartphone through specific mobile application,which later gets processed by  the application provider on the cloud server using automated face detection and recognition technologies.
      10. Thailand: Using the face recognition ability to identify people from group images, Thailand Government is investigating a deadly bombing case on a famous shrine. Such automatic machines can detect up to 100 people within 5-6 seconds than making people sit and do the same job which would take all day.

While this is a small roundup of the Use-Cases where we see Face Recognition technology in use, we would love to hear from you if there have been some glaring omissions.

Tweet: World's Top 10 Usage of Face Recognition Technology #2015 http://ctt.ec/qi076+

 

 

 

World’s Top 10 Usage of Face Recognition Technology #2015

Research in recent face recognition technologies seem to suggest that it is challenging to fake the identity of the person even after a facial surgery (though, we find that hard to believe) because they make use of something called as ‘nodes’ (around 600 nodes for every face) of the facial structure, which these techniques claim, remains unique to each person – dead or alive.

Face recognition has its distinct advantage of being non-contact in nature compared to the other well-known bio metrics. A distant webcam/ video recorder and off lately smart phones, are capturing details required for bio metric systems that use Face Recognition, without any extra effort required by the people whose biometrics are being captured. Coming to think of it, this is in itself is a revolution in terms of the technological advancements that have taken place to enable such a seamless biometric operation to take place.

We have seen that face recognition technology backed products are becoming part of the mainstream. Here are a few use cases of face recognition technology being used to impact societies across different geographies:

Tweet: World's Top 10 Usage of Face Recognition Technology #2015 http://ctt.ec/qi076+

      1. New York & New Jersey, USA: New York and New Jersey are the first states using facial recognition software to bust illegal drivers at the Department of Motor Vehicles (DMV). CBS 2’s Jessica Schneider says it’s the key technological tool now used by law enforcement. It has already helped authorities make 3,500 arrests of the illegal or dual motor license fraudsters.
      2. Bangalore, India: LabourNet Services has adopted SmartAttendance, “a mobile based face recognition solution” to make sure the expected beneficiaries are receiving educational and skill development services at right time and place. As a social enterprise they are able to
        • Control the drop out rates of the enrolled trainees
        • Ensure the presence of expected trainees at the training sessions
        • Automate the complete monitoring process
      3. Arizona,USA:  The number of potential ID fraud cases identified has increased by 860 percent, as reported by the Arizona Republic. The Arizona Department of Transportation (ADOT) has implemented a commercial-off-the-shelf (COTS) vendor-hosted facial recognition solution, to support the Motor Vehicle Division (MVD) credential-issuance process.  This will further enhance fraud and identity theft protection for the citizens of Arizona and over time, expedite the processing and cut the number of fraud investigation cases. The system cross-references new driver’s license pictures with the MVD’s database of existing drivers.
      4. USA: Apple and Facebook have already worked on a mechanism that auto shares the images their social circles based on face recognition
      5. Indonesia, US, Portugal, Africa, India : Using face recognition to identify faces of church visitors from CCTV footage or photos to match churchgoers used to check attendance numbers, and alert church officials if some members stop coming to services. Attendance of these events and services is a key indicator on how the church performs in terms of popularity and growth. It also allows the church to keep a close connection to its members. Most churches already keep track of members attendance manually. However, when it comes to big events it is nearly an impossible task to track members.  It can also screen for people banned from the church.
      6. USA: Customs and Border Protection Officers stationed at air ports of entry are using facial recognition technology as a tool to help them in determining whether an individual presenting themselves with a valid U.S. electronic passport is the same individual photographed in that passport. The U.S. Customs and Border Protection (CBP) is conducting 1:1 Facial Recognition to identify this.
      7. China: A team of university and technology company researchers in China introduced an ATM with built-in facial recognition technology to cut the risk of illegal withdrawals.  The new ATM, built by a team from Tsinghua University and Tzekwan Technology, which captured the facial images using the cameras equipped in the ATM and later compare them with ID photos for verification. The facial recognition measure will be an added layer of identification on top of the traditional password or PIN required to access funds with a card. The ATM will also be linked to banks and local police offices to further bolster security.
      8. India: A social enterprise delivering health services in remote places (rural and semi rural areas) has adopted a novel solution to make sure beneficiaries receive the services at the right place and time through the on-the-field employees. The SmartAttendance, a mobile based face recognition and tracking solution collects bio metric data tagged with GPS and time stamp to detect and identify beneficiaries at meeting venues. This  has enabled the organization to track, monitor and audit the data coming from all the on-site service locations across the country. Salary calculations, productivity analysis and auditing are made extremely simple through the use of visual dashboards.
      9. Europe: Government and law enforcement agencies took a huge technological leap by providing their agents with the ability to identify individuals using facial recognition in ‘near’ real-time. Agents can now transmit photos or videos captured on their smartphone through specific mobile application,which later gets processed by  the application provider on the cloud server using automated face detection and recognition technologies.
      10. Thailand: Using the face recognition ability to identify people from group images, Thailand Government is investigating a deadly bombing case on a famous shrine. Such automatic machines can detect up to 100 people within 5-6 seconds than making people sit and do the same job which would take all day.

While this is a small roundup of the Use-Cases where we see Face Recognition technology in use, we would love to hear from you if there have been some glaring omissions.

Tweet: World's Top 10 Usage of Face Recognition Technology #2015 http://ctt.ec/qi076+

How to begin your career in Computer Vision and Machine Learning Field?

This post is for Computer vision enthusiasts. We are highlighting the focal point of resources and computer vision tutorials for all CV aficionados and get themselves started in this emerging field. As a passionate beginner in the field of computer vision, we hope you find this useful.
Some prior knowledge about linear algebra, calculus, probability and statistics would definitely be a plus but its not always required. The most important thing is to get started and you can learn other essential things on the fly.
Courses:
  • Computer Vision – Mubarak Shah (UCF) : All the materials related to the course are available online and what is more interesting is that even the video lectures are available.
  • Computer Vision – Subhransu Maji (UMass Amherst) : Provides access to all the lecture materials and assignments but there are no video lectures.
  • Visual Recognition – Kristen Grauman (UT Austin) : This Provides links to some of the interesting and fundamental papers in computer vision.
  • Language and Vision – Tamara Berg (UNC Chapel Hill) : This course is basically aimed towards exploring topics straddling the boundary between Natural Language Processing and Computer Vision.
  • Convolutional Neural Networks for Visual Recognition – Fei-Fei Li and Andrej Karpathy (Stanford University) : This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for computer vision tasks with a main focus on image classification.
Some additional resources:

Tweet: How to begin your career in Computer Vision and Machine Learning Field? http://ctt.ec/C03V3+ #AI #career #technology #CV #MLBooks:

Computer Vision
In addition to this, it is very useful to be aware of most of the basic image processing techniques presented in this book Digital Image Processing – Gonzalez 2007
OpenCV Programming
Software Packages:
You can find an exhaustive list of links presenting code which implements some of the standard vision algorithms at http://www.cvpapers.com/rr.html
Major Conferences: 
Below are some of the major conferences listed in their ranking order.
Most of the papers published in the above mentioned conferences can be accessed at http://www.cvpapers.com/index.html
Nice way to keep track of the conferences deadline is via http://conferences.visionbib.com/Iris-Conferences.html
Now that you have acquired some knowledge of computer vision and Deep Learning (from the previous post), please feel free to compete in Kaggle competitions (best way to put your learning into practice).
If you would like to have any guidance/support in CV domain or have any additional resource information, we would love to hear it without judging you. You may drop your comments below.

Tweet: How to begin your career in Computer Vision and Machine Learning Field? http://ctt.ec/C03V3+ #AI #career #technology #CV #ML

Debunking Myths About Computer Vision – Face Recognition via blog.placemeter.com

We loved this blog post from Placemeter. The link to the original post is here.

http://blog.placemeter.com/2014/10/31/debunking-myths-about-computer-vision/

 

Technology is often over-estimated or under-estimated based on what people see in TV shows and movies. Thanks to Mission Impossible, Jack Bauer, and almost any modern police drama out there computer vision has been recently over-estimated.

 

 

As a CV veteran, I always get amused, and sometimes annoyed by the things that happen “on TV.” I also realize that the impression people get from TV and movies can affect how people perceive technologies like these. So in a new series, I’m going to give readers an idea of what current misconceptions of computer visions are.

Myth Number One:

Instant, Universal Face Recognition

Face recognition is a very complex task. The human eye is extremely well trained at recognizing people—faces are the first things children recognize. But physically, two faces are always very, very close. In short, it is a lot harder to recognize someone than to tell a cow from a car in a picture. Today, face recognition works well in two cases:

  1. if the subject is willing to be recognized
  2. if the “dictionary” of faces is relatively limited

The first is the case when an ATM is using your face to identify you and unlock your card. The algorithm in that case can use several images of your face, use longer exposure time, and usually a large amount of pixels to recognize you.

The second is the case where Facebook or Apple is able to recognize your friends or family in your photos. In that case, the total number of people you are trying to recognize is limited, usually < 100.

facebook-facetags

In any case, you need a good amount of pixels to recognize a face, at least 60×60 in general. And you need good pictures. And you need a limited recognition test.

Face detection algorithms need at least a 60x60 pixel sized face to work.

Law enforcement does use scenarios like those, but it involves a lot of manual work: to recognize a terrorist in a stadium, a cutting edge face recognition algorithm could probably suggest thousands of matches out of the tens of thousands of people present, and an officer would have to manually sift through those. These limits were on full display during the Boston marathon bombing when expensive facial recognition systems failed to identify the Tsarneav brothers.

Based on that, two things would not work: recognizing anyone no matter what on Facebook without the context of who is your friend, or instantly recognizing a terrorist in the crowd in a stadium using a drone.

 

Could building systems to track us on the streets improve privacy?

Extending the state’s ability to track you via the millions of CCTV cameras that watch our streets every day would generally be perceived as an erosion of privacy.

In fact, the opposite could be true, says one of the men building the computer systems to automate CCTV monitoring in cities around world.

Cyrille Bataller is managing director with the Accenture’s Emerging Technology group, which recently tested a video analytics system on CCTV around the city-state of Singapore, as part of the government’s Safe City programme.

The system automatically monitored video feeds, creating anonymized data about the movements of people and traffic across the city, monitoring the state of the streets, and triggering alerts for public disorder, flood risks and other incidents

In a video analytics solution a computer doesn’t have any bias,” he said, saying the argument could be extended to using facial recognition technologies to pick out select individuals from a CCTV feed.

“If there’s an operator watching a camera to look for individuals of interest, he’s got a watch list but he can’t help but recognise a politician, a movie star, his sister-in-law. Even if they’re not part of his list he will see them and that’s where the privacy is challenged,” he said.

“[With an automated system] even if the face is a well-known public figure, if it doesn’t match any of the faces in the watch list or in the person’s of interest list they would be ignored. You increase data privacy by removing bias through automation.”

While some may have concerns about authorities mining data about citizens’ daily lives ,  Bataller argues that eliminating manual analysis of CCTV feed reduces the potential for unwarranted snooping.

About Aindra Systems, a start-up with Mobile Visual Identification & Tracking Systems

Aindra Systems is among one of the very few companies in India working in the domain of Artificial Intelligence, specifically Computer Vision creating products which solves the problems of huge magnitude. The current problems addressed by the mobile our systems:

– Identifying people on-the-field, on-the-go to enhance the organization productivity.
– Providing a unified solution for the in office and on-the-field employees.
– OneClick attendance of the students in the classroom of educational institutes.
– Recognizing the valued customers of the luxury retail store as soon as they walk into their premise and creating the heat maps.

Facial Recognition For On-the-field and In-the-office Employees

facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.

It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.

There are numerous biometric security systems in the world. These biometric systems are well adopted and  have proven their utility in multiple scenarios and conditions. Although, no technology is cent percent right but delivering a trust worthy and reliable solution for various use cases has always been a great challenge accepted by the technologist , researchers and entrepreneurs across the world.  Facial recognition system is one of the unique way of biometric identity verification since ages. This system includes many variations like access control system, visitor management system, time attendance system and many more. These applications enhance the security level to many premises such as schools, offices, government buildings, educational institutes, and other organizations. It is not only beneficial for security purposes, but also for controlling human resources by ensuring their presence at right time and location.

The Visual Identification & Tracking Systems (MVITS) is a novel and creative way to track and monitor your on-field and in-office employees/staff. MVITS is a unified solution which ensures both the on field force employees presence at right time and location and simultaneously ensuring the check in  check out time of the in-the-office employees.

Currently the visual identification system is based on the patent pending technology of facial recognition which widely used to record several factors like on-field & in-office presence and absence,location details of the meetings, overtime and under time, authorized and unauthorized leaves, etc. of each employees and staff of the company. This system includes several steps which enables its tracking work. The first step involves the employees to enroll themselves by using their mobile/desktop/laptop devices which has built in web cam . Based on the concept of BYOD– Bring Your Own Device, it captures the facial features of individual employee and uploads to the cloud to build the database. This process of building the database is OTP- One Time Process called “enrolment”.  Next time when the employee/staff  use the application,  it automatically detects and recognises the employee to ensure his/her presence at the right time and location.

Still many small and medium companies are using the pen-paper and log book based attendance system. This is highly time-consuming and effort driven process. This traditional way of ensuring the productivity has always  been prone to problems like data manipulations, proxies, data loss, data mismanagement , lack of analytical information and many more.