Securing Access with AI
Real-Time Face Recognition at Scale
Empowering seamless and secure access control with cutting-edge
machine learning, real-time video processing, and facial recognition technology.
Real-Time Access Control with Live Face Recognition Using Camera Streaming
Overview
An advanced, real-time facial recognition system was required to deliver high-precision identification to secure personnel identification and access to restricted zones. This solution is needed to process live video streams with minimal latency, efficiently detecting and distinguishing individuals by comparing newly captured facial features against a repository of pre-stored vectors. The objective was to enable instantaneous match or no-match determinations, ensuring security and seamless operational flow in high-traffic, access-controlled environments.
ML Architecture
Situation
Challenges arose in achieving high accuracy and low latency in face recognition. With the need for immediate access, the solution had to swiftly process each video frame, detect faces, generate feature vectors, and perform comparisons against an extensive dataset of known faces. Furthermore, the system demanded precise differentiation between recognized and unrecognized individuals, all while minimizing computational load to maintain efficiency in real-time operations.
Task
The primary task was to develop a live face recognition system that could
- Detect faces in real-time from the camera feed.
- Generate accurate feature vectors for each detected face.
- Compare each new vector to an existing set of stored vectors, indicating a match or no match based on a predefined similarity threshold.
- Handle large volumes of real-time data with minimal latency.
Action
To achieve these objectives, the following actions were taken
- Data Collection
Collected images from all employees by capturing 50+ images per person to ensure a comprehensive dataset. Each image was processed to generate facial feature vectors, which were stored as reference vectors for future comparison. - Face Detection and Preprocessing
Integrated OpenCV to capture frames from the camera feed and process each frame in grayscale for efficiency. Faces in each frame were detected using OpenCV’s Haar Cascade classifier, ensuring quick and effective face extraction. - Feature Vector Generation
Used the DeepFace library, leveraging the ArcFace model to create highly accurate facial embeddings of the detected faces. The embeddings were normalized, aligned, and stored for efficient access during real-time matching. - Embedding Comparison
Compared the new embedding with stored embeddings using a weighted similarity function based on cosine distance. This function calculated the distance between vectors and applied a weighted similarity threshold to decide a match. If a vector’s distance exceeded a similarity threshold of 0.97, it was flagged as a close match. The system used a 60% threshold of close matches to determine the final label for each recognized face; otherwise, the label was set as “Unknown.” - Threshold Calibration and Optimization
Conducted data analysis to calibrate the distance threshold. Randomly sampled pairs of vectors were used to create a labeled dataset with “same class” and “different class” labels, allowing fine-tuning of the similarity threshold for optimal matching accuracy. Achieved high accuracy by setting the threshold so that vectors beyond 0.97 were considered a match, ensuring reliable identification even in varying lighting conditions. - Storage and Logging
Results were logged and stored in a database to maintain records of recognized and unrecognized faces, enabling the client to analyze entries over time for security auditing purposes.
Results
The implemented solution successfully achieved the following:
- High Accuracy The use of ArcFace embeddings ensured precise facial recognition, with an accuracy of 94-95% providing reliable identification.
- Low Latency The optimized use of OpenCV and DeepFace allowed real-time processing with minimal delay, meeting the client’s requirement for immediate feedback. .
- Scalability: The solution efficiently handled a large dataset of reference vectors, allowing seamless scaling as the client added more personnel.
The client was satisfied with the accuracy and responsiveness of the solution, as it provided reliable access control with minimal false positives and negatives. This approach demonstrates AWS Machine Learning competency by delivering a scalable, high-performance live face recognition system tailored to meet real-world security requirements.
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