Facial Recognition System Case Study

Challenge

Our client is a Middle East-based IT company that provides custom software solutions to private and government organizations. After securing a government project, they partnered with TatvaSoft to leverage our expertise in biometric technology to develop a facial recognition system. The government’s surveillance department requested that our client automate criminal detection, requiring a high-tech biometric system for criminal identification.

Key Challenges

In this project, we faced the challenge of creating an accurate facial recognition system for a criminal database, as investigative agencies routinely verify suspects against this database for potential criminal records. Some of the technical hurdles we had to overcome include:

Increasing the system's execution speed.
Ensuring the system works effectively with low-resource hardware.
Training the system with limited data while maintaining accuracy.
Enabling real-time detection.

Expertise

Web Architecture

MVT
Web Framework

Flask
Database

MongoDB
Programming Language

Python
Machine Learning Libraries

Keras • TensorFlow • Scikit-learn • NumPy • OpenCV

Solution

The client already had a few fantastic ideas, which were sharpened and flawlessly executed by the AI engineers at TatvaSoft. The key features of the facial recognition system are:

Prediction level indicatorWhen the system detects a person, it displays the accuracy level as a percentage. All details saved about that person in the database also appear on the screen.
Multiple detectionThe prediction system developed by TatvaSoft features multiple face detection capabilities. If it detects more than one face in an image or live camera feed, the GUI will display boxes around each detected face, along with the details and accuracy level of each facial match separately.
Algorithm optionsWe have provided a feature in the system that allows the user to select an option from a list of different algorithms with different accuracy levels.
Data managementUsers can view or edit the samples used to train the ML model. These samples are stored in MongoDB and can be easily removed. More importantly, the GUI displays all selected images to verify the data before it is fed into the ML model as part of the machine-learning process. All additional data is stored in MongoDB, while data extracted from the images for detection purposes is saved in serialized files.
TrainingUsers can utilize existing images or a live camera feed through an easy-to-use graphical interface to train the ML model.

Result

In this collaborative project, we developed a web app with promising, high-recognition results. Delivering this error-free, user-friendly, and highly effective system to the client helped boost their business growth. TatvaSoft’s expertise in AI development and IT services enabled our client to offer an industry-proven, advanced AI solution to the aviation sector.