Learning from the Rare Disease Community: Enhancing Facial Recognition AI Technologies for Improved Accuracy in Identifying Individuals with Facial Deformities
Facial recognition technology has made significant advancements in recent years, transforming various industries and enabling efficient identification and authentication processes. However, one area where these technologies often fall short is accurately recognising individuals with facial deformities. This limitation has profound implications for people with facial deformities, who may experience challenges in accessing services, completing job applications, security checkpoints, and even social interactions. This blog explores how artificial intelligence (AI) could play a pivotal role in improving facial recognition technologies to accurately identify individuals with facial deformities.
Understanding the challenges
Facial deformities encompass a wide range of conditions, including craniofacial anomalies, burns, scars, birth defects, and other facial irregularities due to rare diseases. These conditions can significantly alter an individual’s facial features, making it difficult for conventional facial recognition systems to identify them accurately. Traditional algorithms are often trained on databases that primarily consist of images of individuals with typical facial appearances, resulting in biased models that struggle to recognise those with facial deformities. Amit Ghose, an Asian male born with Neurofibromatosis Type 1 (NF1) resulting in facial deformities, knows only too well the issues with AI and facial recognition:
“I feel that that AI is quite discriminatory. And someone who lacks confidence or someone who is insecure about their appearance already, to have that lack of facial recognition and rejection could be quite detrimental to their already lack of confidence that they have.”
Amit was determined to find out if AI could be improved to help in specific uses such as security at airports. His initial research highlighted that the solution seemed to be to bypass the system, and for those individuals not recognised, they would be escorted manually. For Amit, this felt wrong.
“You can imagine if you’ve got a queue behind you, how humiliated you could feel if the system was still rejecting you. Now, how is that solution just to let people bypass a security feature?”
The Role of AI in Improving Accuracy
Artificial intelligence offers promising solutions to address facial recognition technologies’ limitations when identifying individuals with facial deformities. By leveraging algorithms and advanced machine learning techniques, there is a very real opportunity to drive positive change for inclusivity. And that approach has to include partnership working with those communities that would benefit, as Amit suggests:
“The solution should be that you work with us to enhance the software and the application. You need to work with us to understand and raise awareness of the issues we face. So software companies, you guys need to do something.”
Amit suggests a call to action could include the following:
- Diverse and Inclusive Training Data: To overcome the bias in training data, AI algorithms should be trained on more diverse datasets that include images of individuals with various facial deformities. Collecting and curating extensive databases with labeled images of individuals with facial anomalies can significantly improve the accuracy and reliability of facial recognition systems for this population.
- Transfer Learning: Transfer learning enables the application of pre-trained models that have been trained on large-scale datasets, such as general facial recognition databases. These models can be fine-tuned using smaller, specialised datasets of individuals with facial deformities. By building upon the existing knowledge of facial features, transfer learning can improve the accuracy of recognition for individuals with facial anomalies.
- 3D Facial Recognition: Conventional facial recognition systems primarily rely on 2D images, which may struggle to accurately capture and identify unique facial characteristics in individuals with deformities. 3D facial recognition, utilising depth-sensing cameras or other technologies, can capture the three-dimensional structure of a face, including subtle details. AI algorithms can then be trained to process this data and recognise individuals with greater accuracy, even in the presence of facial deformities.
- Facial Landmark Detection: AI algorithms can be employed to detect facial landmarks, such as the position of the eyes, nose, and mouth, in individuals with facial deformities. By accurately identifying these key points, algorithms can normalise the facial images, compensating for deformities and making recognition more reliable.
- Adaptive Algorithms: AI-powered facial recognition systems should employ adaptive algorithms that continuously learn and adapt to the unique facial features of individuals with deformities. This adaptive approach would allow the system to refine its recognition capabilities over time, enhancing accuracy and reducing false negatives.
- Ethical Considerations: As with any AI application, there are important ethical considerations when it comes to facial recognition technologies for individuals with facial deformities. The utmost care must be taken to ensure privacy, data security, and protection against potential misuse or discrimination. Transparent data collection and model development processes, as well as ongoing audits, can help address these concerns and ensure that the technology is used in an ethical and responsible manner.
A future with AI
Facial recognition technologies hold immense potential to improve the lives of individuals with facial deformities by enabling accurate identification and access to various services. By harnessing the power of AI, through diverse training data, transfer learning, 3D recognition, facial landmark detection, and adaptive algorithms, we can overcome the limitations of current systems.
However, it is crucial to approach the development and deployment of these technologies with a deep commitment to inclusivity, fairness, and privacy. And to work closely with organisations, networks and individuals like Amit who experience and understand the challenges and wish to support AI technologies to develop and recognise them. With continued advancements and ethical considerations, AI can revolutionise facial recognition, providing a more equitable and accessible future for individuals with facial deformities.
“I think when it comes to educating everyone about how to deal with people with visible differences and also with rare diseases is where you can, as much as you can, try and treat them with that dignity, that they are normal and normalise everything.”
We throw down the gauntlet to those working with AI to come and join us at RARESummit23. Amit will join us, alongside a wealth of diverse speakers, set to delve into the rare disease communities’ most pressing questions, scientific advances, and wishes for the future.