Verified page
RekognizeAI
Ethical Facial Recognition Built for People of Color
About RekognizeAI
RekognizeAI is a bias-aware facial recognition system developed to accurately identify people of color. Unlike mainstream algorithms trained on non-representative datasets, RekognizeAI uses a large proprietary dataset and advanced deep learning models to reduce racial bias in face recognition—bringing equity to applications in law enforcement, border security, and humanitarian identification.
The Problem
Current facial recognition systems misidentify people with darker skin tones up to 100× more often, leading to wrongful arrests, service denial, and exclusion from digital systems. This systemic issue reinforces societal inequalities and erodes trust in AI.
The AI Solution
Convolutional Neural Networks (CNNs) trained on inclusive datasets
ResNet + ArcFace models for high-accuracy facial embedding
Bias mitigation algorithms integrated during training
Transfer learning + continual learning to maintain relevance
Distributed training for scalable, fast development
RekognizeAI combines fairness and precision to create a truly inclusive AI platform.
Why It Matters
Facial recognition systems today are not built for everyone. Mainstream models—trained mostly on light-skinned faces—are up to 100 times more likely to misidentify people with darker skin tones. This leads to wrongful arrests, exclusion from public services, and systemic injustice.
RekognizeAI was built to fix this.
By using a large, diverse dataset and state-of-the-art deep learning models (e.g., ResNet, ArcFace), RekognizeAI delivers fair and accurate identity verification—ensuring that AI serves all people equally, especially in law enforcement, border security, and humanitarian contexts.
Business Model
RekognizeAI provides facial recognition as a service (FaaS) to:
Public sector agencies (e.g., police, immigration, refugee agencies)
Private organizations (e.g., financial services, education, healthcare)
NGOs for aid distribution, missing persons, or displaced identity tracking
It monetizes via:
API access with tiered usage plans
Edge device licensing (e.g., Jetson Xavier deployments)
Custom model training for enterprise & government clients
Progress So Far
Working prototype developed and tested
Trained using proprietary, bias-reduced datasets
Implemented continual learning for real-world adaptability
Built a hybrid compute infrastructure:
AWS EC2 + on-prem NVIDIA A100 for training
TensorFlow, PyTorch, and Triton Inference Server
Amazon S3 & Google Cloud Storage for data
Compute Needs: 1,350–3,700 GPU hours/month
Limitation: Slow training cycles due to lack of high-end GPU access
Support & Collaboration
RekognizeAI is open to partnerships with:
Cloud infrastructure providers (for GPU access)
Governments and NGOs committed to digital equity
Ethical AI researchers interested in racial fairness
Investors and accelerators scaling high-impact frontier technologies
“We’re not just building tech. We’re building trust in AI—for everyone. Join us to scale equitable AI that respects diversity and protects dignity.”
Vote for RekognizeAI
Help scale the world’s first bias-aware facial recognition platform for people of color. Your vote ensures AI works for everyone, everywhere.
