Navigating the Nuances: Ensuring Accuracy in Facial Recognition

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Alex Shatalov Data Scientist & ML Engineer

You have probably heard about the rapid progress in AI for analyzing human faces. Facial recognition, emotion detection, age estimation – the list goes on. At first glance, these technologies seem incredibly useful, even magical. However, it is important to understand the limitations of face recognition accuracy to avoid potentially harmful misuse.

As a person interested in artificial intelligence, you might know that AI models are only as good as the data for their training. You may have also heard that AI systems today can match faces or guess emotions but do not understand the nuances of human behavior or psychology.

To deploy AI face analysis responsibly and ethically, you must consider current tech capabilities and limitations. So, where should you start to use AI for face recognition? We suggest learning some practical insights from a real project CHI engineers worked on and then spice it up with tips for improving accuracy of facial recognition. Let’s jump in! 

How Accurate Is Facial Recognition?

We know how hard it is to believe that technology can actually recognize your face. But let’s talk about research and figures instead of trusting our inner guts. So, how accurate is facial recognition?

To start short, you must know that current algorithms provide a nearly 99% accuracy. Not bad, right? Now, let us explain why we’re so sure about this figure.

It is all about the face recognition vendor test (FRVT), a series of face recognition evaluations conducted by the National Institute of Standards and Technology (NIST) throughout the years. The goal of this ongoing testing is to provide the public with a clear understanding about high-quality face recognition algorithms working with hard data (which offers maximum objectivity).

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The tests include algorithms developed both in academic and commercial environments worldwide, now focusing on Face-in-Video-Evaluation (FIVE) and demographic factors, like age or race. This program has no analogs on the market in terms of data volume and algorithms studied, which makes it an invaluable source of information for anyone fascinated by face recognition accuracy and its business potential.

What Does It Take to Build an Accurate AI Facial Recognition App? CHI Software Experience

This story began when a Japanese company providing photo services reached out to our team for an accurate AI face recognition solution.

The biggest challenge of this project was the need to detect and recognize one face in group photos and also small, rotated, and collapsed images. Any AI engineer would tell you that poor image quality is the worst nightmare for face detection projects (see more information in the following article sections). But challenging projects drive our engineers the most and boost our expertise growth. Now, we are proud to have this app in our portfolio.

What We Came Up With: Provided Solution

Here is the full scope of the work that has been done:

1. Multi-service comparison. First, CHI engineers compared different services for the project purposes like Azure, Amazon, and Kairos. Their main focus was to get a better handle on recognizing full faces, figure out how many people were in a photo, and understand the face size in relation to the photodetector.

In this phase, we thoroughly examined existing services, adhering to the age-old wisdom of not ‘reinventing the wheel.’ However, our meticulous review revealed a crucial insight: these existing solutions fell short of meeting our client’s specific requirements. This realization steered us towards a more tailored approach.

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2. Setting up cloud infrastructure and storage. We set up a cloud environment for both developing and hosting the project. Our team went with AWS EC2 for the infrastructure platform and also used AWS S3 for storage.

Choosing AWS EC2 and S3 suggests a focus on scalability and reliability. EC2 offers flexible compute capacity essential for processing large volumes of image data, while S3 provides secure and scalable object storage.

3. Enhancements and system development. The CHI team significantly improved how the system recognized faces, especially those turned or tilted between 45 and 90 degrees. We also got better at recognizing backgrounds and faces that were either really small or big.
4. Automating monitoring and manual processes. Another great thing we did was automate a bunch of stuff. As you surely know, the usual monitoring and manual processes can be a bit of a drag. Our engineers managed to make those more efficient.
5. Providing ongoing support and maintenance. Once we integrated everything into the system, we, of course, did not leave it at that. Our team continues to provide support services to make sure everything runs smoothly. 

If you are looking for more tech details, do not hesitate to read this case study in full.

As we always say, there are no similar AI projects. Each has its own peculiarities, like the ones we just described. If you want a more detailed look behind the curtains, let us explore specific tips and techniques for precise AI detection and recognition.

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Understanding the Challenges of AI Face Recognition Accuracy

Face recognition using artificial intelligence has come a long way, but ensuring accuracy remains an ongoing challenge. Many factors can impact the engineer’s work, including:

• Image quality: Low-resolution, poorly lit, or obscured images are harder to analyze accurately. Always use high-quality, well-lit images for the best results;
• Skin tone and age: Many datasets for training AI systems lack diversity, focusing primarily on lighter skin tones and younger faces. This can lead to higher error rates for people of color or older individuals. Seek out systems trained on diverse, representative datasets;

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• Emotional expression: AI struggles to interpret faces displaying strong emotions like anger, fear or disgust. Neutral or slight smiles tend to yield the most accurate analysis;
• Multiple faces: Images containing more than one face are prone to incorrect identifications or can confuse the AI system. For optimal precision, analyze images of a single face whenever possible;
• Profile views: Faces turned to the side or at an angle are more challenging to analyze precisely due to obscuring portions of the face. Front-facing images are ideal.

Being aware of these common challenges and mitigating them whenever possible can significantly enhance precision in AI face analysis. With ongoing progress, AI will continue to get better at overcoming these obstacles, but human judgment still plays an important role.

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How to Improve Face Recognition Accuracy

There are a few tips in addition to the ones mentioned above that you can employ for AI face recognition quality control. The most popular are the following.

• Use advanced neural networks. AI models like convolutional neural networks, recurrent neural networks, and transformers are purpose-built for computer vision and natural language processing. They can detect subtle features the human eye might miss. Ensemble models that combine multiple neural networks also tend to perform better.
• Employ data augmentation. Techniques like cropping, flipping, color jittering, and noise injection can artificially expand your dataset and make the model more robust. The model will learn to recognize faces even when details are obscured, or the image is distorted.
• Regularly retrain your model. Retrain the AI model from scratch as your dataset grows with new images. This helps it continue learning and improving, capturing the latest facial features, attributes and qualities it needs to recognize. Retraining also reinforces the model’s knowledge so it does not forget what it has already learned.

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AI face analysis can become remarkably accurate with diligent effort focused on precision. But, it is still an ongoing process that requires close monitoring and continual refinement.

5 Steps for Bias Reduction in AI Facial Recognition

These simple steps are a sure way to provide accuracy and mitigate bias in AI facial recognition models.

Step 1: Address Data Gaps

As mentioned earlier, the data for training facial recognition models must represent people of all backgrounds. The model may struggle with AI facial identity accuracy of face recognition if certain groups are underrepresented in the training data. At the same time, more bias will emerge if some groups are overrepresented. So, make sure to collect a broad dataset representing each age, gender, nationality, and other demographic factors equally.

Step 2: Choose Diverse Teams

It is not only about datasets. You must also have diverse, inclusive teams build and evaluate facial recognition models. These teams should include people of different ethnicities, genders, ages, and backgrounds. Diverse perspectives can help identify harmful biases and make models more equitable.

Step 3: Examine the Data for Bias

Carefully analyze the dataset to uncover any biases. For example, look for disproportionate mislabeling or exclusion of certain groups. Address any issues found to avoid perpetuating unfair stereotypes or judgments.

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Step 4: Validate Models for Fairness

Rigorously test models before deploying them to ensure fair and unbiased performance across all groups. If any disparities are detected, refine the model to resolve them. Independent third-party audits can also help determine if models meet face recognition accuracy standards.

Step 5: Monitor Models Post-Deployment

Even after models are deployed, continue evaluating them to catch emerging issues. Monitor how different groups are impacted to ensure you use reliable AI face detection methods. Be transparent about how models are monitored and updated to build trust.

Mitigating bias and ensuring inclusiveness in AI facial recognition is an ongoing process. With continuous refinement and oversight, you can ensure the desired accuracy of face recognition. 

Conclusion

As you have seen, AI face analysis validation requires a thoughtful and nuanced approach to achieve the desired results. 

By embracing diversity, validating across groups, and monitoring for unfairness, you can responsibly unlock the promise of AI for face recognition. Trustworthy AI for face analysis has the potential to improve security, enhance user experience, and gain useful insights, but only if you are willing to invest the time to do it right. As an experienced image recognition software company, CHI Software is here to help you with that.

Having a strong passion for innovations, we are a team that would not be the same without our knowledge and facial recognition development services we provide. You have just read it in our case study, and we will gladly share more of our skills with you.

So, stop hesitating. Just schedule a free call with our experts.

About the author

Alex Shatalov Data Scientist & ML Engineer

Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.