Best Face Detection Software

Biometric is the technology used to identify and authenticate individuals quickly and reliably using biological characteristics.

There are various techniques for biometrics like fingerprint scanning, iris recognition, palm vein recognition, voice recognition, and face recognition. Whether they are operating solo or in combination, they make excellent tools in authenticating a person’s identity. Their adoption rate largely relies on user’s convenience and operation speed; considering these attributes face recognition is quite popular among the users.  It has emerged as a promising option for various organizations to identify individuals with high accuracy and minimize the risk of identity theft. Face recognition systems give a sense of security compared to traditional techniques like smart cards, PINs, plastic cards, passwords, tokens, keys, etc. Face detection is a computer-based technology that has its roots in Artificial Intelligence. 

The face identification is categorized into 4 categories,

1. Feature-based: The feature-based method is to locate faces by extracting the structural features of the face.
2. Appearance-based: The appearance-based approach uses statistical and machine learning techniques to learn the characteristics of face and non-face images from examples. 
3. Knowledge-based: The knowledge-based face detection method depends on the set of rules, based on human knowledge to detect the faces. 
4. Template matching: Template matching is a digital image processing method for finding small parts of an image that matches a template image. The template matching is used to detect the more accurate faces and neglect the others. The technique may involve edge detection, feature extraction, and object extraction. 

Once the face detection process is completed using any of the above methods, face recognition biometrics carries out the further identification and verification process.

Identity theft is like a treasure hunt for hackers; once they succeed in replicating identity, the damage they can bring is unaccountable. For instance, filing fraudulent income tax returns and applying for loans under the victim’s name. It can lead to financial loss and threatens a victim’s reputation. Despite all the precautionary measures taken by individuals, they were never far from the next incident of identity theft.  The traditional methods are powerless against such hackers. The only way to defend against these attacks is to use the uniquely detectable biological traits like face recognition or fingerprint scanning. Compared to other biometric systems, face recognition biometrics does not require physical contact with the device. It requires less processing time and helps to automate the authentication process.

Face recognition technology has immense potential to modernize the current identification system. And as per a few reports, the global biometric facial recognition market is estimated to surpass $12 billion by 2026. There is a rising demand for biometric surveillance systems among private and government institutes. Face recognition establishes a high level of security, with a minimum amount of time or effort. It is difficult to manipulate the facial features or steal the information. The face recognition system equipped with 3D facial recognition can accurately identify a number of different facial expressions and postures, regardless of the illumination level. The system can be easily deployed in a dark environment. 

Biometrics is just one area, but businesses are taking face recognition capabilities into new dimensions. The retail industry sees facial recognition as the ground-breaking technology in rendering personalized experience to the customer. Likewise, other sectors like law enforcement, logistics, immigration, and healthcare are equally excited about the face recognition applications in their respective fields.

Yes, compared to the existing identification process, the face recognition technology can bring remarkable improvements.  

The advanced technology within the face recognition tool is designed to compare and predict potential matches of faces regardless of their age, expression, and facial hair. The contactless facial recognition system speeds up the identification process and eliminates all the risk associated with existing identification processes.

• PIN lost or stolen: The most common person identification and verification method is using the PIN system (Personal Identification Number). The risk with such techniques is that the person can forget the PIN, or it can be stolen for misuse.
• Compromised passwords: The basic form of user authentication on the web is password authentication. If the individual doesn't have strong server security, someone can easily break into the database and read the passwords.
• Smartcards unauthorized access: Smartcards are protected by a two-factor authentication process that involves showing the card and then entering a password or PIN code. Again with PIN or password access, the risk of a security breach cannot be ignored.
• Hacking token-based authentication: The token-based authentication is the most secure form of authentication currently, as it is laced with two-factor authentication. But smartphone-based tokens sent as texts are riskier because they can be intercepted during transit.

Yes, there is software available for conducting facial identification.

Besides facial recognition, facial identification technology acts as the first step in many key applications like face tracking and face analysis. Face recognition tools like Face++ offer a range of features like face searching, face comparing, 3D face model reconstruction, including face detection to meet customer’s requirements. The user can use these features individually, or together as per the application requirement. In case if the users are not keen to develop the system from scratch, they can go for a ready-to-use face recognition system. 

There are also tools like Ever AI that give liberty to customize their face recognition system by using APIs.

Face recognition software is a technology capable of verifying or identifying a subject through an image, video, or any audiovisual element of his face. The advanced face recognition software even allows users to find people in real-time.

There are two main jobs of facial recognition systems:  

• The first is verification, where the input image is compared with the known identity. Unlocking mobile devices with facial identification is the best example where new input faces are compared with the registered face on the device. 
• The second is the identification where an input face is compared to a database of multiple face identities. It is mostly used for security and surveillance purposes. 

Although face recognition seems simple, identifying a particular face is actually a challenging task. .With the advancement of computer and artificial intelligence, the algorithm of face recognition has become smarter. It parses huge data in a few seconds and retrieves information with a high level of accuracy level.

Face recognition has a vast application in different sectors, and in various ways, it helps them to address their concerns.

• Fast identity verification: The face recognition technology facilitates quick identification of persons compared to traditional methods. The system enables them to verify identities without stopping anyone for a check. Face identification technique is much easier than entering a complex password or pin number several times a day.
• Requires no ID cards: It takes the burden away from individuals to carry documents or ID cards for identity verification. It is very helpful in colleges and universities.
• Reduction of fraudulence: The face recognition software reduce the instance of fraudulence since there is no paper document involved and unlike the paper document the identity of an individual can’t be tampered 
• Data storage flexibility: The data can be accessed and analyzed by the authority immediately whenever it is required. There is no need to search files in cabinets or cluttered racks. 
• Substituting tickets and tokens: Facial recognition is replacing conventional ticketing systems and repetitive security checkpoints. For any small or big events, the technology could be very productive and cost-saving.
• Secure Payment: Facial recognition can be used to automate payment processes and authorize contactless payments. Just by scanning the face, the payment can be made.   
• Aiding law and enforcement: Facial recognition has a wide application in law enforcement, instantly identifying criminals in the field from a safe distance as well as they can maintain their record easily.
• Smart advertising: Traditional marketing was transformed with the introduction of digital marketing, and now digital marketing is ready to get transformed with face recognition software. The face recognition software can help brands to target their customers according to age or gender.
• Control access to restricted areas: In certain organizations, few areas are restricted zones, and only authorized people can access them. In such situations, rapid and secure facial recognition could be an advantage.

Deep Learning is an AI-based machine learning technique dealing with algorithms that simulate the human brain’s neural networks. Their intelligent algorithm and neural network can play a key role in enhancing the performance of face recognition tools in terms of speed and accuracy.

• Use pre-trained models:  The pre-trained models already have a set of algorithms for face recognition purposes. It offers significant time and cost savings.
• CNN (Convolutional Neural Networks): A CNN is a type of Deep Neural Network (DNN) that is optimized for complex tasks such as image processing, which is required for facial recognition. It is very effective in image classification and recognition. Due to the COVID-19 pandemic break out, most people have to put a mask on their faces, the data science engineer used the CNN technique to identify the face behind the mask. They got 95% accuracy with this technique.
• Supporting neural networks: Deep learning also equips with other techniques to improve neural networks for facial recognition systems like transfer learning, knowledge distillation, quantization, and depth-separable convolutions.

Deep learning has powered face recognition software to its advanced form of learning human emotions. The deep learning algorithm can accurately identify the landmark points of a human face, and facial expressions differentiating the positive or negative ones. 

Step 1: Face Detection

The camera will detect and recognize a face, either in a crowd or alone. The face detection process is a critical step as it detects and locates human faces in videos or images. The advanced face recognition system even allows capturing images in real-time. 

Step 2: Face Analysis

Once the face is detected, the photo of the face is captured and analyzed.  The face is analyzed on various nodal points such as spacing of the eyes, bridge of the nose, the shape of your cheekbones, the contour of the lips, ears, chin, etc.  Humans have 80 nodal points.

Step 3: Converting an image into code

In this step, the nodal points of the face are converted into a mathematical formula. The image is now converted into its digital form, also known as a faceprint. For each individual, the system generates a unique face print. 

Step 4: Face match

Face match verifies whether two faces belong to the same person. The face print of numerical code is compared against the database of other faceprints. If there is any match, the system will retrieve the image along with the name and address.

• Age detection
• Face analysis
• Face captureS
• Spoof detection
• Alerts/Notifications
• Emotion detection
• Face clustering
• Gender detection
• Identity verification
• Facial coordinates
• Privacy protection
• Diversity recognition
• Real-time fetching and processing of the images
• Sharing data across unlimited locations
• Multi-Angle Face Scanning
• Unlimited Face Detection

1. The current facial recognition system may require high computing power to process various sizes of images. But with the rise of mobile devices, the future facial recognition system may have to run on limited computing power. 
2. The second alternative to suffice a facial recognition system having high computing power is to shift the system to cloud storage. With the increasing internet connectivity, cloud computing could be a leverage for the mobile user to access the face recognition system.
3. The future attempt may also be towards refining the distorted image and identifying them accurately. The future facial recognition system may be designed to convert poor quality 2D images into 3D models.
4. Tech geeks are optimists about improving the face recognition system performance by synchronizing them with wearables like Google glass. Some companies have already started developing kits to embed with wearables.
5. Face detection has already empowered the security and surveillance sector. The mobile devices, laptops, and tablets are actively implementing them for secure access and maintaining user’s privacy. In the future, face detection techniques would be embedded with security Robots to conduct remote surveillance and identity check. These Robots would be placed in sensitive areas like banks, hospitals, and airports.

With recent advancements in deep learning, the face recognition system has achieved 99% accuracy with a false acceptance rate of 1 in thousand. But there are many factors like surveillance camera or phone camera quality that determines the accuracy rate.. 

Facebook’s DeepFace uses facial recognition to automatically identify and send notifications to friends or relatives when someone in the group uploads the photos. It has a 97% accuracy rate. 

Even the National Institute of Standards and Technology (NIST) claimed that the best face identification algorithm in 2020 has an error rate of just 0.08% compared to 4.1% for the leading algorithm in 2014.

As per a few reports, the facial recognition market is projected at $3.2 billion and is estimated to grow to $7 billion in revenue by 2024. The prices of face detection software vary according to the number of features and the volume of data to be processed. Different companies may have different price plans; there are software vendors that give flexibility on payment with their pay-as-you-go plans. The user can expect a price range to $99 with tools like Kairos , while Face++ may go as high as $10,500 or more. There are many other tools available like Deep Vision, Churchix, Sentinel that give provision to use the free trial. Some software vendors prefer one-to-one interaction to discuss pricing or send quotations on demand. 

There are free and open source face recognition software solutions that could add to the above list and give users to explore more options on face recognition systems.

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Face recognition is envisaged as the revolutionary technology to lessen the risk associated with identity fraud. Hope this article helped you gain enough knowledge about this technology. Do not forget to check GoodFirms' list of best Face Detection Software to prevent unauthorized access to your premises, documents, or personal information.