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Face Recognition Project, Assignment  Help

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Facial recognition is a way of recognizing a human face through technology. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. Facial recognition can help verify personal identity, but it also raises privacy issues.

We can  perform face recognition in both images and video streams using:

  • OpenCV

  • Python

  • Deep learning

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

In python, there is a library to perform such task and here is command to install  pip install face-recognition

Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library.

Built using dlib’s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the

Labeled Faces in the Wild benchmark. This also provides a simple face_recognition command line tool that lets

you do face recognition on a folder of images from the command line!

How Facial Recognition works

Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person's facial details.

  • The face detection process is an essential step as it detects and locates human faces in images and videos.

  • The face capture process transforms analogue information (a face) into a set of digital information (data) based on the person's facial features.

  • The face match process verifies if two faces belong to the same person.


Today it's considered to be the most natural of all biometric measurements. 

And for a good reason – we recognize ourselves not by looking at our fingerprints or irises, for example, but by looking at our faces. 

Face recognition solutions

  • Digital identity

  • Connected consumer

  • Public safety

  • Business insights

Digital identity solution

Individual identity represented through digital means with greater safety, security and privacy

  • Authentication

  • Access Control

  • In-Person ID Verification

  • Online ID Verification

  • Age Verification

Connected consumer solution

Omnichannel engagement that enables customer experiences and software enabled transactions

  • Omnichannel Integration

  • Consumer Engagement

  • Personal Digital Signage

  • Loyalty/eCoupons

  • People Counting

Public safety solution

Machine learning algorithms trained to aid in crime prevention and public safety

  • Loss Prevention

  • Organized Retail Crime

  • Fraud Mitigation

  • Real-Time Alerting

  • Security Integration

Business insights solution

Real-time alerts and actionable analytics providing expansive insights

  • Sentiment Scoring

  • Traffic Analytics

  • Audio Analytics

  • Fraud Analytics

  • Privacy Compliance

Face Recognition Applications/Use Cases

Face Identification: 

Face recognition systems identify people by their face images. Face recognition systems establish the presence of an authorized person rather than just checking whether a valid identification (ID) or key is being used or whether the user knows the secret personal identification numbers (Pins) or passwords.

To eliminate duplicates in a nationwide voter registration system because there are cases where the same person was assigned more than one identification number. The face recognition system directly compares the face images of the voters and does not use ID numbers to differentiate one from the others. When the top two matched faces are highly similar to the query face image, manual review is required to make sure they are indeed different persons so as to eliminate duplicates.

Access Control: 

In many of the access control applications, such as office access or computer logon, the size of the group of people that need to be recognized is relatively small. The face pictures are also caught under natural conditions, such as frontal faces and indoor illumination. The face recognition system of this application can achieve high accuracy without much cooperation from the user.


Face recognition technology is used to monitor continuously who is in front of a computer terminal. It allows the user to leave the terminal without closing files and logging out. When the user leaves for a predetermined time, a screen saver covers up the work and disables the mouse & keyboard. When the user comes back and is recognized, the screen saver clears and the previous session appears as it was left. Any other user who tries to log in without authorization is denied.


Today more than ever, security is a primary concern at airports and for airline staff offices and passengers. Airport protection systems that use face recognition technology have been implemented at many airports around the world.

In October 2001, Fresno Yosemite International (FYI) airport in California deployed Viisage's face recognition technology for airport security purposes. The system is designed to alert FYl's airport public safety officers whenever an individual matching the appearance of a known terrorist suspect enters the airport's security checkpoint. Anyone recognized by the system would have further investigative processes by public safety officers. Computer security has also seen the application of face recognition technology. To prevent someone else from changing files or transacting with others when the authorized individual leaves the computer terminal for a short time, users are continuously authenticated, checking that the individual in front of the computer screen or at a user is the same authorized person who logged in.

 Image database investigations:

Searching image databases of 

  • licensed drivers

  • benefit recipients

  •  missing children

  • Immigrants

  • police bookings. 

General identity verification: 

Electoral registration, banking, electronic commerce, identifying newborns, national IDs, passports, employee IDs. 



Like security applications in public places, surveillance by face recognition systems has a low user satisfaction level, if not lower. Free lighting conditions, face orientations and other divisors all make the deployment of face recognition systems for large scale surveillance a challenging task. The following are some example of facebased surveillance. 

To enhance town center surveillance in Newham Borough of London, this has 300 cameras linked to the closed circuit TV (CCTV) controller room. The city council claims that the technology has helped to achieve a 34% drop in crime since its facility. Similar systems are in place in Birmingham, England. In 1999 Visionics was awarded a contract from the National Institute of Justice to develop smart CCTV technology. 

Diagnose Diseases

Face recognition can be used to diagnose diseases that cause detectable changes in appearance. 

The National Human Genome Institute Research Institute, uses face recognition to detect a rare disease called DiGeorge syndrome, in which there is a portion of the 22nd chromosome missing. Face recognition has helped diagnose the disease in 96% of cases. As algorithms get even more sophisticated, face recognition will become an invaluable diagnostic tool for all sorts of conditions.

Protect Schools From Threats

Face recognition surveillance systems can instantly identify when expelled students, dangerous parents, drug dealers or other individuals that pose a threat


In the world, a countless number of people are missing every day which includes kids, teens, mentally challenged, old-aged people, etc. Most of them remain untraced. Users can upload a missing person's photo and the platform can match it with missing people in the database and  can become alerted as soon as they are recognized by face recognition—be it an airport, retail store or other public space.



In addition to making schools safer, face recognition has the potential to track students’ attendance. Face recognition-based attendance software which identifies students at the classroom and keeps track of their course attendance.



Facial recognition can aid forensic investigations by automatically recognizing individuals in security footage or other videos. Face recognition software can also be used to identify dead or unconscious individuals at crime scenes.



Face recognition can be used to provide fans with a better experience. Face recognition can instantly recognize when season ticketholders attend sporting events. Event venues can offer them swag, let them skip lines and other VIP perks that result in greater season ticketholder retention.



Since face recognition is such a powerful identity authentication tool that can be used to make sure that individuals using ATMs cards are who they say they are.  Face recognition is currently being used at ATMs in many cities in India to protect peoples’ identities. 

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