Deep Learning for Face Recognition | How to Improve It

Deep Learning for Face Recognition | How to Improve It
What is a face tracker? Is it a time bomb or a significant technology trend? In this article, we will explain face tracking from a technological point of view to better understand how a facial recognition security system improves its capabilities.

Face tracking software allows mapping and analyzing faces in a photo or video to confirm someone’s identity. Picture recognition software is one of the most powerful surveillance tools. Many people use a face detection app to unlock their smartphones or sort photos. But the way organizations and governments implement this technology will have a greater influence on people’s lives.

When you have a device that uses the software, you can turn it off or switch off the facial recognition feature. However, the ubiquity of cameras makes it very hard for people to avoid the technology in public spaces. For example, during the Black Lives Matter movement, the concerns about racial profiling and protester identification forced large businesses to stop selling the software to law enforcement authorities. However, the moratorium that had prohibited sales has since expired, and machine learning facial recognition is now better and cheaper.

How Does Facial Recognition Work

Many people see face tracking software in movies where the technology is not often depicted correctly, so don’t rely on your impressions from that medium for insights. We’ll give you a quick overview here.

It is possible to divide the process into three fundamental types of technology:

1. Detection

Detection helps to find a face in the image. For instance, if you have ever taken photos with a camera that draws a specific box for auto-focusing, you have already seen this technology in action. In this case, the goal of facial recognition software is only to locate the face to focus on it for the picture, not to identify a person.

2. Analysis

This attribution step allows for the mapping of faces by measuring the distance between the individual’s eyes, the one between the nose and mouth, and the shape of the chin. That results in converting these measurements into a string of numbers or points, also known as a faceprint. Although the analysis may have some glitches, including misidentification, it becomes problematic only when the faceprint is added to the recognition database.

3. Recognition

Recognition is where an individual’s identity is confirmed using a photo. This process is widely used for verification as an additional security feature on your smartphone or for identification, and it helps answer the question: “Who is the person in this picture?”.

The detection stage of picture recognition software begins with a specific algorithm learning to recognize a face in general. The algorithm’s creator trains the program with photos of faces. For example, if you provide a sufficient number of photos to train the algorithm, soon it will learn the difference between face and other objects like a wall outlet. Then you add an algorithm for analysis and another one for recognition, and you will get an efficient face recognition system.

The diversity of photos added to the system can affect its accuracy significantly, especially during the analysis and recognition stages. For example, most sample sets of early face recognition machine learning systems involved caucasian people. That makes it very hard for the technology to accurately identify faces of women and black, indigenous and people of color (BIPOC). The best face recognition software these days is intended to correct this, although caucasian people continue to be falsely matched in fewer cases compared to other groups because there is more data on them.

After you train your software to detect and recognize faces, it will find and compare them with other available faces in your database. This process is called the identification step since the face analysis app accesses the existing databases that include photos and cross-references them to identify an individual based on various sources. Then the software shows the results and often ranks them by accuracy. Although such systems may sound complicated, you can create a facial recognition security system with the help of some technical skills and typical software.

Facial Recognition Software Principles

There are two major facial recognition software principles:

  • Using pre-trained models

Among these models are dlib, DeepFace, FaceNet, and others. This principle means that you need to spend less time and effort since pre-trained models already have a suite of algorithms for deep face recognition purposes. You can also fine-tune these models to avoid bias and help your face recognition system perform better.

  • Developing a neural network from scratch

A neural network can fit more complex face recognition systems that have multi-purpose functionalities. Developing a neural network takes more time and effort, and it requires millions of pictures in the training dataset while using pre-trained models only requires thousands of images.

If your face tracking software involves unique features, that is optimal. In this case, the critical points to analyze are:

  • Selecting a correct convolutional neural networks (CNN) architecture and loss function
  • Optimizing the inference time
  • Assessing the capabilities and power of the hardware

It is recommended applying CNN for network architecture development, since they have already proven their effectiveness in image recognition and other classification tasks. To achieve the desired results, you should choose a traditional neural network architecture such as ResNet or EfficientNet.

What is Deep Learning?

Deep learning is among the most novel approaches to improving current face recognition technology. It requires extracting face embeddings from pictures with faces. And the most appropriate way to conduct this task is the training of neural network facial recognition.

The crucial component of face recognition deep learning is the presence of high-powered hardware. If you use deep neural networks for developing face recognition software, the objective is to enhance recognition accuracy and reduce the response time.

How Deep Learning Improves Face Recognition Technology

There are some approaches to how deep learning can improve face recognition technology. Below we provide a brief description of the key ones.

  • Knowledge distillation

It includes two networks of different sizes, and the larger one serves as a teacher for its smaller variation. After the training, the smaller one can work faster compared to the larger network, providing the same result.

  • Transfer learning

This particular approach enhances the accuracy by training the entire network or just some layers related to a specific dataset. For instance, if your faces race bias issues, you can choose a particular suite of pictures that depict many races and train your network to obtain higher accuracy.

  • Quantization

It enhances the face recognition technology to reach a higher processing speed. With the approximated neural network that applies for floating-point numbers, you can cut down the memory size as well as the number of computations.

  • Depthwise separable convolutions

Such convolutions represent a class of layers that allow one to create a CNN with a much smaller suite of parameters than the standard CNNs have. With a few computations, such a feature enhances the facial recognition system by making it suitable for mobile apps.

Facial Recognition Challenges and Advantages

Supporters of facial recognition claim that face tracking software helps identify suspects, monitor known criminals and identify victims of child abuse. For instance, deep face recognition can monitor suspects in crowds at various events, and improve security at public spaces like airports.

There can be multiple benefits of facial recognition apart from law enforcement since it adds convenience and security to different routine actions and experiences. You can use facial recognition to organize photos, secure devices such as smartphones or laptops, or even assist blind and low-vision communities. Face recognition deep learning offers more secure options for entering business places, preventing fraud at ATMs, registering during events, and accessing online accounts. Commercial apps that use facial recognition provide numerous benefits as they can track customer behavior in stores for personalizing ads online.

On the other hand, the opponents of face recognition deep learning do not think that the advantages are worth the privacy risks, and they do not trust both the systems and the people who run them. Law enforcement authorities can collect images very easily: it is difficult for people to avoid having their photos taken. After all, there are cameras everywhere. Another problem is error rates in recognition, which may cause innocent individuals to be falsely identified. Also, face recognition used by law enforcement authorities cannot be audited publicly.

Meanwhile, the algorithms that empower detection and identification software solutions serve as closed-box proprietary systems making it impossible for researchers to investigate them. If people do not know how these facial recognition systems perform and what accuracy they provide, they do not understand whether such systems are being used correctly.

In this case, society should address critical questions about deep learning face recognition regulation, the services for its usage, and the privacy sacrifices that everyone can agree on.

Solutions for Deep Learning Technologies for Face Recognition

Thanks to rapid advancements in artificial intelligence (AI), machine learning (ML) and deep learning technologies, the face recognition industry is growing. Algorithms currently allow for finding, capturing, storing, and analyzing facial features for further matching with the pictures of people in a pre-existing database.

Among the most popular solutions of deep learning technologies for face recognition are:

  • Google Photos/Apple Photos

The ability to organize photos has been the first experience with facial recognition for many individuals. In the case of Apple, their technology is more private than a cloud server, but cloud-based software provides more accuracy. What about Google Photos? It offers very accurate face grouping. However, since the company has multiple services and devices, it may share data through the available services.

  • Unlocking a phone or computer

Smartphone features allow you to use your face to unlock the device, but the data cannot be uploaded to a specific server or added to a relevant database.

  • Home security cameras

There is a lack of consent in software systems behind security cameras since they allow for recording and opting people in automatically. Only some home security cameras currently involve facial recognition.

Conclusion

Cprime Studios offers the leading solutions for automating and enhancing digital identity verification and customer onboarding thanks to the best face recognition software powered by the newest advancements in AI and ML technologies. Our experienced team has the appropriate expertise and passion to create comprehensive machine learning solutions and bring them to market successfully.