Face recognition using Artificial Intelligence

Last Updated : 10 Jun, 2023

The current technology amazes people with amazing innovations that not only make life simple but also bearable. Face recognition has over time proven to be the least intrusive and fastest form of biometric verification. The software uses deep learning algorithms to compare a live captured image to the stored face print to verify one’s identity. Image processing and machine learning are the backbones of this technology. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airports, criminal detection, face tracking, forensics, etc. Compared to other biometric traits like palm print, iris, fingerprint, etc., face biometrics can be non-intrusive.

They can be taken even without the user’s knowledge and further can be used for security-based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face images from a video or a surveillance camera. They are compared with the stored database. Face recognition involves training known images, classifying them with known classes, and then they are stored in the database. When a test image is given to the system it is classified and compared with the stored database.

Face recognition

Face recognition using Artificial Intelligence(AI) is a computer vision technology that is used to identify a person or object from an image or video. It uses a combination of techniques including deep learning, computer vision algorithms, and Image processing. These technologies are used to enable a system to detect, recognize, and verify faces in digital images or videos.

The technology has become increasingly popular in a wide variety of applications such as unlocking a smartphone, unlocking doors, passport authentication, security systems, medical applications, and so on. There are even models that can detect emotions from facial expressions.

Difference between Face recognition & Face detection

Face recognition is the process of identifying a person from an image or video feed and face detection is the process of detecting a face in an image or video feed. In the case of Face recognition, someone’s face is recognized and differentiated based on their facial features. It involves more advanced processing techniques to identify a person’s identity based on feature point extraction, and comparison algorithms. and can be used for applications such as automated attendance systems or security checks. While Face detection is a much simpler process and can be used for applications such as image tagging or altering the angle of a photo based on the face detected. it is the initial step in the face recognition process and is a simpler process that simply identifies a face in an image or video feed.

Image Processing and Machine learning

Image processing by computers involves the process of Computer Vision. It deals with a high-level understanding of digital images or videos. The requirement is to automate tasks that the human visual systems can do. So, a computer should be able to recognize objects such as the face of a human being or a lamppost, or even a statue.

Image reading:

The computer reads any image in a range of values between 0 and 255. For any color image, there are 3 primary colors – Red, green, and blue. A matrix is formed for every primary color and later these matrices combine to provide a Pixel value for the individual R, G, and B colors. Each element of the matrices provide data about the intensity of the brightness of the pixel.

OpenCV is a Python library that is designed to solve computer vision problems. OpenCV was originally developed in 1999 by Intel but later supported by Willow Garage.

Machine learning

Every Machine Learning algorithm takes a dataset as input and learns from the data it basically means to learn the algorithm from the provided input and output as data. It identifies the patterns in the data and provides the desired algorithm. For instance, to identify whose face is present in a given image, multiple things can be looked at as a pattern:

Height/width of the face.
Height and width may not be reliable since the image could be rescaled to a smaller face or grid. However, even after rescaling, what remains unchanged are the ratios – the ratio of the height of the face to the width of the face won’t change.
Color of the face.
Width of other parts of the face like lips, nose, etc.

There is a pattern involved – different faces have different dimensions like the ones above. Similar faces have similar dimensions. Machine Learning algorithms only understand numbers so it is quite challenging. This numerical representation of a “face” (or an element in the training set) is termed as a feature vector. A feature vector comprises of various numbers in a specific order.

As a simple example, we can map a “face” into a feature vector which can comprise various features like:

Height of face (cm)
Width of the face (cm)
Average color of face (R, G, B)
Width of lips (cm)
Height of nose (cm)

Essentially, given an image, we can convert them into a feature vector like:

Height of face (cm) Width of the face (cm) Average color of face (RGB) Width of lips (cm) Height of nose (cm)

23.1 15.8 (255, 224, 189) 5.2 4.4

So, the image is now a vector that could be represented as (23.1, 15.8, 255, 224, 189, 5.2, 4.4). There could be countless other features that could be derived from the image,, for instance, hair color, facial hair, spectacles, etc.

Machine Learning does two major functions in face recognition technology. These are given below:

Deriving the feature vector: it is difficult to manually list down all of the features because there are just so many. A Machine Learning algorithm can intelligently label out many of such features. For instance, a complex feature could be the ratio of the height of the nose and the width of the forehead.
Matching algorithms: Once the feature vectors have been obtained, a Machine Learning algorithm needs to match a new image with the set of feature vectors present in the corpus.
Face Recognition Operations

Face Recognition Operations

The technology system may vary when it comes to facial recognition. Different software applies different methods and means to achieve face recognition. The stepwise method is as follows:

Face Detection: To begin with, the camera will detect and recognize a face. The face can be best detected when the person is looking directly at the camera as it makes it easy for facial recognition. With the advancements in technology, this is improved where the face can be detected with slight variation in their posture of face facing the camera.
Face Analysis: Then the photo of the face is captured and analyzed. Most facial recognition relies on 2D images rather than 3D because it is more convenient to match to the database. Facial recognition software will analyze the distance between your eyes or the shape of your cheekbones.
Image to Data Conversion: Now it is converted to a mathematical formula and these facial features become numbers. This numerical code is known as a face print. The way every person has a unique fingerprint, in the same way, they have unique face prints.
Match Finding: Then the code is compared against a database of other face prints. This database has photos with identification that can be compared. The technology then identifies a match for your exact features in the provided database. It returns with the match and attached information such as name and address or it depends on the information saved in the database of an individual.

Implementations

Steps:

Import the necessary packages
Load the known face images and make the face embedding of known image
Launch the live camera
Record the images from the live camera frame by frame
Make the face detection using the face_recognization face_location command
Make the rectangle around the faces
Make the face encoding for the faces captured by the camera
if the faces are matched then plot the person image else continue

Python3

   # Import the necessary packages
import cv2 as cv
import face_recognition
import matplotlib.pyplot as plt
 
 
# Load the known image 
known_image = face_recognition.load_image_file("pawankrgunjan.jpeg")
known_faces = face_recognition.face_encodings(face_image = known_image,
                                              num_jitters=50,
                                              model='large')[0]
 
# Lanch the live camera
cam = cv.VideoCapture(0)
#Check camera
if not cam.isOpened():
    print("Camera not working")
    exit()
     
# when camera is opened
while True:
     
    # campture the image frame-by-frame
    ret, frame = cam.read()
     
    # check frame is reading or not
    if not ret:
        print("Can't receive the frame")
        break
 
    # Face detection in the frame
    face_locations = face_recognition.face_locations(frame)
 
    for face_location in face_locations:
        top, right, bottom, left = face_location
        # Draw a rectangle with blue line borders of thickness of 2 px
        frame = cv.rectangle(frame,  (right,top), (left,bottom), color = (0,0, 255), thickness=2)
    # Check the each faces location in each frame
    try:
        # Frame encoding
        Live_face_encoding = face_recognition.face_encodings(face_image = frame,
                                                              num_jitters=23,
                                                              model='large')[0]
 
        # Match with the known faces
        results = face_recognition.compare_faces([known_faces], Live_face_encoding)
 
        if results:
            img = cv.cvtColor(frame, cv2.COLOR_BGR2RGB)
            img = cv.putText(img, 'PawanKrgunjan', (30, 55), cv2.FONT_HERSHEY_SIMPLEX, 1,
                    (255,0,0), 2, cv2.LINE_AA)
            print('Pawan Kumar Gunjan Enter....')
            plt.imshow(img)
            plt.show()
            break
    except:
        img = cv.putText(frame, 'Not PawanKrgunjan', (30, 55), cv2.FONT_HERSHEY_SIMPLEX, 1,
                (255,0,0), 2, cv2.LINE_AA)
        # Display the resulting frame
        cv.imshow('frame', img)
        # End the streaming
        if cv.waitKey(1) == ord('q'):
            break
     
 
# Release the capture
cam.release()
cv.destroyAllWindows()
 
  

Output:

Pawan Kumar Gunjan Enter....

Face Recognization

The model accuracy further can be improved using deep learning and and other methods.

Face Recognition Softwares

Many renowned companies are constantly innovating and improvising to develop face recognition software that is foolproof and dependable. Some prominent software is being discussed below:

a. Deep Vision AI

Deep Vision AI is a front-runner company excelling in facial recognition software. The company owns the proprietorship of advanced computer vision technology that can understand images and videos automatically. It then turns the visual content into real-time analytics and provides very valuable insights.

Deep Vision AI provides a plug and plays platform to its users worldwide. The users are given real-time alerts and faster responses based upon the analysis of camera streams through various AI-based modules. The product offers a highly accurate rate of identification of individuals on a watch list by continuous monitoring of target zones. The software is highly flexible that it can be connected to any existing camera system or can be deployed through the cloud.

At present, Deep Vision AI offers the best performance solution in the market supporting real-time processing at +15 streams per GPU.

Business intelligence gathering is helped by providing real-time data on customers, their frequency of visits, or enhancement of security and safety. Further, the output from the software can provide attributes like count, age, gender, etc that can enhance the understanding of consumer behavior, changing preferences, shifts with time, and conditions that can guide future marketing efforts and strategies. The users also combine the face recognition capabilities with other AI-based features of Deep Vision AI like vehicle recognition to get more correlated data of the consumers.

The company complies with international data protection laws and applies significant measures for a transparent and secure process of the data generated by its customers. Data privacy and ethics are taken care of.

The potential markets include cities, public venues, public transportation, educational institutes, large retailers, etc. Deep Vision AI is a certified partner for NVIDIA’s Metropolis, Dell Digital Cities, Amazon AWS, Microsoft, Red Hat, and others.

b. SenseTime

SenseTime is a leading platform developer that has dedicated efforts to create solutions using the innovations in AI and big data analysis. The technology offered by SenseTime is multifunctional. The aspects of this technology are expanding and include the capabilities of facial recognition, image recognition, intelligent video analytics, autonomous driving, and medical image recognition. SenseTime software includes different subparts namely, SensePortrait-S, SensePortrait-D, and SenseFace.
SensePortrait-S is a Static Face Recognition Server. It includes the functionality of face detection from an image source, extraction of features, extraction, and analysis of attributes, and target retrieval from a vast facial image database
SensePortrait D is a Dynamic Face Recognition Server. The capabilities included are face detection, tracking of a face, extraction of features, and comparison and analysis of data from data in multiple surveillance video streams.
SenseFace is a Face Recognition Surveillance Platform. This utility is a Face Recognition technology that uses a deep learning algorithm. SenseFace is very efficient in integrated solutions to intelligent video analysis. It can be extensively used for target surveillance, analysis of the trajectory of a person, management of population and the associated data analysis, etc
SenseTime has provided its services to many companies and government agencies including Honda, Qualcomm, China Mobile, UnionPay, Huawei, Xiaomi, OPPO, Vivo, and Weibo.

c. Amazon Rekognition

Amazon provides a cloud-based software solution Amazon Rekognition is a service computer vision platform. This solution allows an easy method to add image and video analysis to various applications. It uses a highly scalable and proven deep learning technology. The user is not required to have any machine learning expertise to use this software. The platform can be utilized to identify objects, text, people, activities, and scenes in images and videos. It can also detect any inappropriate content. The user gets a highly accurate facial analysis and facial search capabilities. Hence, the software can be easily used for verification, counting of people, and public safety by detection, analysis, and comparison of faces.

Organizations can use Amazon Rekognition Custom Labels to generate data about specific objects and scenes available in images according to their business needs. For example, a model may be easily built to classify specific machine parts on the assembly line or to detect unhealthy plants. The user simply provides the images of objects or scenes he wants to identify, and the service handles the rest.

d. FaceFirst

The FaceFirst software ensures the safety of communities, secure transactions, and great customer experiences. FaceFirst is secure, accurate, private, fast, and scalable software. Plug-and-play solutions are also included for physical security, authentication of identity, access control, and visitor analytics. It can be easily integrated into any system. This computer vision platform has been used for face recognition and automated video analytics by many organizations to prevent crime and improve customer engagement.

As a leading provider of effective facial recognition systems, it benefits to retail, transportation, event security, casinos, and other industry and public spaces. FaceFirst ensures the integration of artificial intelligence with existing surveillance systems to prevent theft, fraud, and violence.

e. Trueface

TrueFace is a leading computer vision model that helps people understand their camera data and convert the data into actionable information. TrueFace is an on-premise computer vision solution that enhances data security and performance speeds. The platform-based solutions are specifically trained as per the requirements of individual deployment and operate effectively in a variety of ecosystems. The software places the utmost priority on the diversity of training data. It ensures equivalent performance for all users irrespective of their widely different requirements.

Trueface has developed a suite consisting of SDKs and a dockerized container solution based on the capabilities of machine learning and artificial intelligence. The suite can convert the camera data into actionable intelligence. It can help organizations to create a safer and smarter environment for their employees, customers, and guests using facial recognition, weapon detection, and age verification technologies.

f. Face++

Face++ is an open platform enabled by the Chinese company Megvii. It offers computer vision technologies. It allows users to easily integrate deep learning-based image analysis recognition technologies into their applications.
Face++ uses AI and machine vision in amazing ways to detect and analyze faces, and accurately confirm a person’s identity. Face++ is also developer-friendly being an open platform such that any developer can create apps using its algorithms. This feature has resulted in making Face++ the most extensive facial recognition platform in the world, with 300,000 developers from 150 countries using it.
The most significant usage of Face++ has been its integration into Alibaba’s City Brain platform. This has allowed the analysis of the CCTV network in cities to optimize traffic flows and direct the attention of medics and police by observing incidents.

g. Kairos

Kairos is a state-of-the-art and ethical face recognition solution available to developers and businesses across the globe. Kairos can be used for Face Recognition via Kairos cloud API, or the user can host Kairos on their servers. The utility can be used for control of data, security, and privacy. Organizations can ensure a safer and better accessibility experience for their customers.
Kairos Face Recognition On-Premises has the added advantage of controlling data privacy and security, keeping critical data in-house and safe from any potential third parties/hackers. The speed of face recognition-enabled products is highly enhanced because it does not come across the issue of delay and other risks associated with public cloud deployment.
Kairos is ultra-scalable architecture such that the search for 10 million faces can be done at approximately the same time as 1 face. It is being accepted by the market with open hands.

h. Cognitec

Cognitec’s FaceVACS Engine enables users to develop new applications for face recognition. The engine is very versatile as it allows a clear and logical API for easy integration in other software programs. Cognitec allows the use of the FaceVACS Engine through customized software development kits. The platform can be easily tailored through a set of functions and modules specific to each use case and computing platform. The capabilities of this software include image quality checks, secure document issuance, and access control by accurate verification.

The distinct features include:

A very powerful face localization and face tracking
Efficient algorithms for enrollment, verification, and identification
Accurate checking of age, gender, age, exposure, pose deviation, glasses, eyes closed, uniform lighting detection, unnatural color, image, and face geometry
Fulfills the requirements of ePassports by providing ISO 19794-5 full-frontal image type checks and formatting

Utilization of Face Recognition

While facial recognition may seem futuristic, it’s currently being used in a variety of ways. Here are some surprising applications of this technology.

Genetic Disorder Identification:

There are healthcare apps such as Face2Gene and software like Deep Gestalt that uses facial recognition to detect genetic disorders. This face is then analyzed and matched with the existing database of disorders.

Airline Industry:

Some airlines use facial recognition to identify passengers. This face scanner would help save time and to prevent the hassle of keeping track of a ticket.

Hospital Security:

Facial recognition can be used in hospitals to keep a record of the patients which is far better than keeping records and finding their names, and addresses. It would be easy for the staff to use this app and recognize a patient and get its details within seconds. Secondly, can be used for security purposes where it can detect if the person is genuine or not or if is it a patient.

Detection of emotions and sentiments:

Real-time emotion detection is yet another valuable application of face recognition in healthcare. It can be used to detect emotions that patients exhibit during their stay in the hospital and analyze the data to determine how they are feeling. The results of the analysis may help to identify if patients need more attention in case they’re in pain or sad.

Problems and Challenges

Face recognition technology is facing several challenges. The common problems and challenges that a face recognition system can have while detecting and recognizing faces are discussed in the following paragraphs.

Pose: A Face Recognition System can tolerate cases with small rotation angles, but it becomes difficult to detect if the angle would be large and if the database does not contain all the angles of the face then it can impose a problem.
Expressions: Because of emotions, human mood varies and results in different expressions. With these facial expressions, the machine could make mistakes to find the correct person’s identity.
Aging: With time and age face changes it is unique and does not remain rigid due to which it may be difficult to identify a person who is now 60 years old.
Occlusion: Occlusion means blockage. This is due to the presence of various occluding objects such as glasses, beard, mustache, etc. on the face, and when an image is captured, the face lacks some parts. Such a problem can severely affect the classification process of the recognition system.
Illumination: Illumination means light variations. Illumination changes can vary the overall magnitude of light intensity reflected from an object, as well as the pattern of shading and shadows visible in an image. The problem of face recognition over changes in illumination is widely recognized to be difficult for humans and algorithms. The difficulties posed by illumination condition is a challenge for automatic face recognition systems.
Identify similar faces: Different persons may have a similar appearance that sometimes makes it impossible to distinguish.

Disadvantages of Face Recognition

The danger of automated blanket surveillance
Lack of clear legal or regulatory framework
Violation of the principles of necessity and proportionality
Violation of the right to privacy
Effect on democratic political culture

Deep Face Recognition

shreya_garg

Improve

Article Tags :

Practice Tags :

Machine Learning

Face recognition using Artificial Intelligence

Face recognition

Difference between Face recognition & Face detection

Image Processing and Machine learning

Image reading:

Machine learning

Implementations

Python3

Similar Reads

Introduction to Computer Vision

Image Processing & Transformation

Feature Extraction and Description

Deep Learning for Computer Vision

Object Detection and Recognition

Image Segmentation

3D Reconstruction