Utilizing AI To Find Missing People: Exploring Deep Learning Facial Recognition Solutions

In America, over 500,000 to 600,000 people go missing every year. The US Department of Justice states that California alone reports around 3400 missing people in a single year. A lot of these cases include missing children who have aged significantly since their disappearance. With so many missing persons, finding ways to identify them efficiently is imperative. Traditional ways of locating missing persons often involve a lengthy interrogation process at police stations. This puts a lot of stress on the missing person’s family and still, the chances of locating the person are very minimal. In this scenario, Deep learning facial recognition is such technology that offers a beacon of hope. It is designed to search through the large amounts of footage and images. And then compare them to databases of missing persons, even if the person’s appearance has changed over time. 

AI-powered missing person search is a game changer in reuniting missing people with their families. So let’s try to understand how this technology works step by step.

How Deep Learning Facial Recognition Technology Works?

1. Data Collection

Let’s take a scenario of a mission person’s case. The family members of that missing person will provide the most recent photo to the law enforcement agency. This image will be used as the reference image.  And will play a crucial role in the missing person search. 

2. Facial Detection

The systems will search person by face with the assistance of a deep learning model. This model has been trained to identify faces in the images. So understand this; the Facial recognition technology model is a program trained on countless pictures and learning what a face looks like. So if an image comes that is blurred or at a different angle, the model can instantly recognize and find the face. The machine would analyze the reference image with the images of the missing persons in the database. 

3. Feature Extraction

Once the face gets detected, the facial technology uses the deep learning model to extract the specific features of the face. The features mainly include;

• Facial thirds
• Hairline
• Distance between the eyes
• A short or long ramus etc.

All of these measurements formulate the “facial fingerprint” of the individual or the missing person in this case. Once the measurements are extracted, the Image matching system will assess the reference image’s face. And then utilize it for 1:N identification in the unidentified person database. 

4. Matching and Identification

The face recognition system is designed to compare the extracted features from the reference image, i.e. the missing person, to the features that are extracted from the unidentified person images. Now once compared, there would be a higher probability of a match, if the facial features are similar. This would instantly flag the system for further investigation.

5. Alert and investigation

Once the image has been identified or if there is a potential match, the face detection system will alert the law enforcement agencies, or in some cases family members. The location of the camera footage will be intimated along with the picture of the missing person.  However, there is one thing that needs to be remembered, this notification is a lead and not a confirmation. So once the close match is notified from a public transportation hub or a shopping mall, for instance, the law enforcement services would investigate further to see if the individual also matches other minor or major details about the missing person. 

Important Considerations

So these were the main steps involved in finding missing people using deep learning facial recognition technology. However, certain factors need to be remembered. This tech is not flawless. Issues like light variations or low-quality images can often hamper the results’ accuracy. So during the investigation process, this technology can offer assistance but does not provide a guaranteed solution. On the other hand, ethical considerations about privacy and data use also impede the success rate of this technology. 

Read More: Pedestrian Detection through AI-Facial Recognition: Securing Crowds with AI

Challenges of Finding Missing Persons and How Deep Learning Facial Recognition Can Address Those

Finding missing persons is an arduous task. Law enforcement agencies and the concerned families have to face many challenges. Let’s discuss what these challenges are and how we can find missing persons using facial recognition. 

6. Limited Information

The biggest challenge faced by law enforcement agencies is that there is very limited information available. Nobody can tell the whereabouts of the missing individual. And even if they do offer their help, it often leads to a wild goose chase. In this scenario, Facial recognition of missing persons can assist in identifying people from images or videos captured by surveillance cameras. Moreover, Deep learning machines can also analyze social media posts which can help aid in locating people who may have been captured on camera, especially in public spaces or maybe particular events.

7. Time Sensitivity

Time constraints always play a crucial role in missing persons cases. If the person goes missing for a long time, it usually becomes very hard to find them. Hence, immediate action sometimes becomes very necessary. The delays caused in reporting can hamper the efforts of the forces. In this scenario, deep-learning image recognition technology can come in to save time. It can analyze large volumes of images and videos in a matter of minutes so that agencies can effectively process leads.

8. False Leads and Tips

Another factor that also presents a lot of challenges is when police forces have to deal with a bunch of tips and leads which often are very misleading. The forces investigate every lead as it is part of the procedure. This not only consumes more time but also leads to the utilization of a lot of resources. So in this case, missing person identification technology can be used to create age-progressed images of the missing persons. This will showcase how the said individual has progressed with age. Ultimately, false leads and tips would be discarded.

9. Legal Barriers

Many states in the United States have strict data privacy laws. GPDR which stands for General Data Protection Regulation limits the use of missing people face matching tech. And hence storing and collecting data on missing persons investigation is limited. This tech itself cannot resolve the legal barriers, however, it can play a huge role in establishing trust within the legal framework. By ameliorating the transparency of how the technology works, especially when it comes to collecting data mitigates ant legal concerns or in some cases false positives. 

Read More: Facial Recognition Technology | A Blazing Firewall To Stop Shoplifting Before It Happens

The Future of Finding Missing Persons With The Aid of Deep Learning Facial Recognition 

Missing person identification technology has the potential to revolutionize police investigations. This tech can scan public camera footage in real time which can potentially diminish the search time and offer faster recoveries. On top of improving the efficiencies of critical cases, it can also transcend geographical boundaries. 

International databases can be integrated with liveness detection which will ensure a swift search and rescue of missing persons. Moreover, repatriation efforts will be dealt with effectively. But within these efforts, chances are that the identification of vulnerable populations could face discrimination. Facial recognition solutions are often condemned for being racially biased. This is a grave cause of concern for the future of this technology and if not resolved it defeats the purpose of unbiased data extraction and interpretation. 

But there are segments of the vulnerable populations such as people with dementia or any other disability that are not in the right state to communicate effectively. The deep learning models of the solutions can analyze the missing person picture whose appearance might have changed with time. Now missing person cases that have not been solved for decades can be addressed via this technology. 

Any advanced technology brings a fair share of challenges. Similarly, deep-learning facial recognition solutions have to address challenges related to regulations, ethics, and biases. As Dr. Alistair Sutherland, a biometric researcher at the University of Surrey states “Facial recognition technology has the potential to be a powerful tool for law enforcement, but it’s important to ensure it is used ethically and effectively” 

Wrapping Up

Conclusively, facial recognition technology of facia plays an important role in lost person recovery cases. There are privacy concerns and some legal hurdles that challenge the effectiveness of such solutions. Moreover, racial biases hamper progress as well. Therefore, regulatory bodies, facial recognition solution providers, and state governments need to work together to ameliorate the technology so that it can become a valuable asset in finding missing persons.