A US-based security technology company specializing in biometric authentication and surveillance systems partnered with Dserve AI to enhance the performance of its face recognition models. Their solutions were deployed across airports, corporate offices, fintech applications, and access control systems where high accuracy and speed were critical.
However, their AI model struggled with real-world variations such as lighting conditions, facial angles, and diverse demographics.
Project Objective
The client required a large-scale, high-quality annotated image dataset to improve recognition accuracy, reduce false matches, and support global deployment.
Primary Goals:
- Deliver 75,000 annotated biometric images
- Improve face detection and recognition accuracy
- Reduce false positives and false negatives
- Support diverse demographics and environments
- Enhance performance in low-light and angled images
- Maintain strict data quality standards
Key Challenges
Building reliable biometric datasets required precision, diversity, and compliance with ethical AI standards.
| Challenge | Description |
|---|---|
| Variation in Lighting | Low light, shadows, and glare affected detection |
| Pose & Angle Issues | Side profiles and tilted faces reduced accuracy |
| Demographic Bias | Limited diversity impacted fairness |
| Occlusions | Masks, glasses, and accessories blocked features |
| High Precision Requirement | Small annotation errors impacted model output |
| Data Consistency | Maintaining uniform labeling across dataset |
Our Solution
Dserve AI developed a structured annotation pipeline to create high-quality biometric datasets tailored for face recognition systems.
What We Delivered:
- 75,000 annotated facial images
- Bounding boxes for face detection
- Landmark annotation (eyes, nose, mouth)
- Multi-angle face coverage
- Diverse demographic representation
- Masked and unmasked face data
- Indoor and outdoor image variations
- Quality-validated datasets
Annotation Workflow:
- Data preprocessing and filtering
- Facial landmark labeling
- Multi-level QA validation
- Bias balancing across datasets
- Final export in model-ready format
Project Impact
After training with the new dataset, the client achieved significant improvements in model performance.
| Metric | Improvement |
|---|---|
| Face Recognition Accuracy | +42% |
| False Positive Rate | -33% |
| Detection in Low Light | +28% |
| Multi-angle Recognition | +35% |
| Model Reliability | +31% |
The stronger AI model delivered direct operational and financial value to the client.
Results Achieved:
- Reduced fraud-related financial losses
- Better customer trust and satisfaction
- Faster response to suspicious transactions
- Lower manual review workload
- Improved regulatory confidence
- Scalable fraud monitoring for growth markets
Dserve AI delivered highly accurate and diverse biometric datasets that significantly improved our face recognition performance. Their attention to detail and quality control is outstanding.
— James Carter, Director of AI Solutions, USA
Why Dserve AI?
Dserve AI is a trusted partner for building high-quality AI training datasets.
Our Strengths:
- Expertise in biometric and computer vision datasets
- High-precision annotation workflows
- Scalable dataset production
- Fast turnaround time
- Strict quality assurance processes
- Custom dataset solutions
Get Your Dataset Sample
Looking for high-quality datasets for biometric AI, computer vision, NLP, or healthcare AI?
Request a free sample dataset from Dserve AI and evaluate our quality before scaling.
Talk to our healthcare AI experts
Medical expert annotations