How face recognition works: embeddings, thresholds, verification vs identification, tools

Face recognition detects a face, aligns it, converts it into an embedding, a vector of numbers describing facial features, and compares that embedding with stored ones using a similarity threshold. Good systems never need to store face images at all. I built FaceVision, which runs detection, recognition and liveness checks in the browser with ONNX Runtime Web and stores only embeddings in PostgreSQL, never images, so this page explains how it works, key terms, alternatives, libraries and real-world uses.

How does face recognition work with embeddings and thresholds?

A detector finds the face, landmarks align it, and a neural network converts it into an embedding, often 128 or 512 numbers. Recognition compares embeddings by cosine similarity or distance: if similarity exceeds a threshold, the faces are considered a match.

Also asked as: how face recognition works: embeddings, thresholds and why you never store faces

The threshold trades false matches against false rejections, so tune it on labelled pairs from your users and conditions. Since matching needs only embeddings, systems can avoid storing face images, reducing harm if data leaks and simplifying privacy compliance. FaceVision follows this design: images are processed in the browser with ONNX Runtime Web, only embeddings reach the FastAPI and PostgreSQL backend, and liveness checks help block photos or screens held up to the camera.

What is the difference between face identification, verification and recognition?

Face verification is one-to-one: is this person who they claim to be, as in phone unlock. Face identification is one-to-many: who is this person among a gallery of known people. Face recognition is the umbrella term covering both.

Also asked as: what is the difference between face identification, face verification, and face recognition

Verification compares a probe embedding with one enrolled template and applies a threshold. Identification compares against many templates, returning the best match above a threshold or no match, and its error rates grow with gallery size, so thresholds must be stricter. Face detection, finding faces in an image, is a separate earlier step and does not identify anyone. Identification raises bigger privacy and legal concerns, so it needs clear consent, purpose limits and oversight.

How can you do face recognition without neural networks?

Classic methods include Eigenfaces, which uses principal component analysis on face images; Fisherfaces, which uses linear discriminant analysis; and Local Binary Patterns Histograms, which describes local texture. OpenCV's face module implements all three.

Also asked as: how to do face recognition without using any kind of neural networks like cnn

These methods need aligned, well-lit, frontal faces and work best on small, controlled datasets, such as a few dozen people in consistent lighting. Detection can also be done without neural networks using Haar cascades, or HOG features with a linear classifier, as in dlib. Accuracy is far below modern deep embeddings on real-world images with varied poses and lighting, so use classic methods for learning, constrained hardware or teaching demonstrations rather than security-critical systems.

Can a face recognition system be trained only on synthetic faces?

Yes, partly. Research datasets such as Microsoft's DigiFace-1M and other generated-face datasets have trained recognition models without real identities, reducing privacy concerns, but models trained only on synthetic faces usually perform worse than those trained on real faces.

Also asked as: can a face recognition system be trained using only computer generated hyper realistic faces

The gap comes from differences in texture, lighting, expressions and demographic variety between synthetic and real images. Combining synthetic data with a smaller real dataset, or fine-tuning on real data, narrows it. Synthetic data also helps balance demographics and add pose or accessory variations that real datasets lack. If you go synthetic-first, evaluate thoroughly on real benchmarks and across demographic groups, since performance differences between groups matter greatly for fairness.

How do you add new people to face recognition without retraining?

With embedding-based recognition, you do not retrain to add people: compute embeddings for a new person's enrolment photos and add them to the gallery. The pretrained embedding model already generalises to unseen faces.

Also asked as: how can i implement continual (incremental) learning in a face-recognition model without retraining from scratch

Store several embeddings per person, or their average, captured under different angles and lighting, and update them over time as appearance changes, for example after verified successful matches. Retraining or fine-tuning the embedding network is only needed when performance on your population or conditions is poor, and then techniques such as replaying samples from earlier data help avoid catastrophic forgetting. This enrolment approach is how FaceVision and most modern systems scale to new users.

OpenCV vs face_recognition: which should you use?

OpenCV is a broad computer vision library; its face modules provide detection and classic recognisers, plus a DNN module for modern models. face_recognition is a simple Python library built on dlib that detects faces and computes 128-dimensional embeddings in a few lines.

face_recognition is great for quick prototypes and learning, with functions to locate faces, compute encodings and compare them with a tolerance. Its dlib model is older and less accurate on difficult images than modern models. OpenCV offers more control, video handling and deployment options, and can run newer detectors such as YuNet and recognisers such as SFace. For production, consider modern embedding models through InsightFace or ONNX Runtime, with liveness detection added.

DeepFace vs face_recognition: which is better?

DeepFace is a Python framework that wraps many face recognition models, such as ArcFace, FaceNet and VGG-Face, and detectors, plus analysis of age, emotion and other attributes. face_recognition provides one dlib-based model with a very simple API.

DeepFace lets you compare models and pick a stronger one, such as ArcFace, often giving better accuracy than face_recognition's default model, at the cost of heavier dependencies. face_recognition remains easier for beginners. For either, set thresholds on your own data and measure false accept and false reject rates. Be careful with attribute analysis such as emotion or gender prediction, which is unreliable and raises ethical concerns; focus on consented, clearly scoped use cases.

Iris vs face recognition: which is more accurate?

Iris recognition is generally more accurate and stable than face recognition, because iris patterns are highly distinctive and change little over life. Face recognition is more convenient, works at a distance and with ordinary cameras, but is affected by pose, lighting, ageing and expression.

Iris systems need near-infrared cameras and close, cooperative capture, which suits border control, national ID enrolment and high-security access. India's Aadhaar programme captures iris scans and fingerprints alongside face photos. Face recognition suits phones, attendance and access where convenience matters. Multimodal biometrics combine methods for higher assurance. Both require liveness or presentation attack detection to resist photos, masks or printed irises, and careful privacy safeguards.

What makes the best face recognition door lock?

Look for locks with 3D or infrared face recognition, which resists photos and works in darkness, local processing on the device, a backup unlock method such as a PIN or key, and strong encryption for stored templates.

Check reviews for recognition speed and false rejections in real conditions, battery life, and how the lock behaves when power or Wi-Fi fails. Prefer brands that publish security practices and provide firmware updates. Avoid locks that rely only on 2D cameras without liveness, which photos can sometimes fool. Consider privacy: understand whether face data leaves the device and whether cloud features can be disabled. For rentals or shared homes, get consent from everyone enrolled.

What is a good face recognition tutorial?

A practical tutorial path: detect faces with OpenCV or a modern detector, compute embeddings with face_recognition or InsightFace, enrol a few known people, then match new images by cosine similarity with a tuned threshold.

Also asked as: tutorial face recognition

Then extend it: process webcam video, add a simple liveness check such as blink or head-movement prompts, store embeddings in PostgreSQL or a vector store instead of images, and measure accuracy on your own test photos. Courses in computer vision cover the theory, while hands-on projects teach thresholds and failure cases. Always use consenting participants, explain how data is handled, and follow local laws, since face data is sensitive personal information in many jurisdictions.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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