Securing Visa Applications: AI-Driven Video Analytics for Threat and Incident Detection

# Data Analytics
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Highlights

  • AI-Powered Video Surveillance: Enhanced with computer vision, object detection, and face recognition to ensure a higher level of security for the visa center system.
  • Detection of Suspicious Activity: Object detection prevents bribery and document forgery, while face recognition detects fraudsters.
  • 40% Improvement in Application Flow: Object tracking allows allocating more personnel in case of overcrowding.

Client

Our client is a visa center specializing in the non-judgmental and administrative management of visa, passport, and consular service applications for governments in 67 countries. Their goal is to streamline government services, making them faster and more efficient. This commitment is evident in their extensive network of 3,398 application centers operating across 151 countries. Since 2001, the company has efficiently processed over 285 million applications, including over 133.95 million biometric enrollments.

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Product

The product is a surveillance system for monitoring and managing the flow of people, preventing fraud, and ensuring security for the visa center. Its core component is a monitoring board powered by AI video analytics. Other features include suspicious activity detection, crowd monitoring, face recognition, and bio-enrollment process violation checks. Leveraging those features, the system evaluates crowd levels, manages customer interactions, and ensures staff compliance with procedures in real-time. In case of anomalies, the system notifies the visa center personnel with in-built alert mechanisms.  

Goals and objectives

  1. Suspicious Activity Detection: Utilize computer vision so that the visa center system could monitor service kiosks for signs of bribery or document fraud. This might involve detecting objects like money being exchanged or identifying suspicious document handling.
  2. Reduce Overcrowding: Leverage computer vision and object detection on videos to analyze customer behavior and the number of people in the visa center. This enables dynamic adjustments, such as opening additional service booths, to minimize wait times and improve customer flow. 
  3. Ensure Incident Management: Track people’s movement with the help of object, person, and fall detection for immediate assistance.
  4. Fire Exit and Pathway Monitoring: Monitor movements in the visa center to ensure fire exits and walkways remain clear of obstructions, facilitating safe evacuation in case of emergencies.
  5. Establish Compliance with Bio-Enrollment Processes: Customize the system for processing fingerprints and use anti-spoofing techniques for face recognition.

Project challenge

  1. Crowd Monitoring and Management: Existing computer vision solutions for crowd monitoring often require significant customization and fine-tuning of ML algorithms to achieve optimal performance of the system. 
  2. Critical Personnel Monitoring: Existing ML models for monitoring specific personnel require additional data collection efforts like labeling video data for training. The integration of such monitoring capabilities also require setting up cost-intensive infrastructure.
  3. Face Recognition for Suspicious Individuals: Creating a blacklist for facial recognition of fraudsters required additional efforts for collecting sufficient data (photos).
  4. Incident Management: Training an accurate AI model required a large amount of labeled data depicting different incident scenarios.

Solution

To ensure the effectiveness of our AI-powered video analytics system, we trained AI models on large datasets. This involved using data from CCTV camera footage, which was later labeled by our data labeling team. To ensure the quality and reliability of the data, our team conducted continuous data quality control sessions and provided immediate feedback to ensure accurate labeling.

Once the data met our quality standards, we proceeded to develop the AI/ML video analytics solution. This solution incorporates the following key components:

  1. Video Analytics: The system used object detection and tracking to monitor the movement of individuals within the visa center. This helped in detecting overcrowding, occupancy counting, jumping queues, and identifying suspicious activities. This component utilized various AI/ML algorithms and models to achieve its goals: MoViNets, the family of convolutional neural networks, helped with video scene recognition, including movement analysis; the YOLO algorithm enabled real-time person detection within CCTV footage, processing each video frame and identifying individuals; StrongSORT facilitated re-identification of specific individuals.
  2. Face Recognition: The system employed face recognition technology to identify suspicious individuals for flagging them for further investigation and visa center personnel for compliance with procedures.
  3. Incident Management: The system employed object detection and person fall detection to ensure the safety and security of individuals within the visa center.

Tech Stack

  • Python Python
  • PyTorch PyTorch

Our results

We delivered the AI-powered video analytics system by customizing multiple ML-models to meet the unique needs of the business. Our efforts resulted in:

  1. Automated Surveillance: Thanks to AI-driven computer vision, object and people detection, the visa center can enhance security of application processing and reduce incidents.
  2. 40% Increase in Applicant Throughput: Thanks to AI-powered monitoring, the visa center can handle crowd management and significantly reduce wait times for applicants.
  3. Significant Cost Savings: By preventing fraud, detecting anomalies, and optimizing staff allocation, the system reduces losses and improves resource utilization.
  4. More Accurate Compliance:  Utilizing robust bio-enrollment processes with fingerprints and face recognition simplifies compliance with regulations.
  5. Better Customer Experience: With proactive real-time monitoring, the visa center can allocate more staff in case of increasing number of applicants and reduce queues.
  6. Data-Informed Insights: The system’s data analytics capabilities provide valuable insights into customer behavior and operational patterns, enabling informed decision-making.
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