FACE Recognization with geocoding

Question

I want to understand the pricing structure of Azure Cognitive Services Face API for my use case.

My use case:

• Employee attendance system using face recognition

Around 100–500 employees

Each employee will punch attendance 2 times per day (in/out)

Using Face Detection and Face Identification APIs

I need clarification on:

How many API transactions are counted per punch?

Estimated monthly cost for my usage

Difference in pricing between Face Detection, Identification, and Verification

Whether face storage and training incur additional charges

Cost impact of enabling Face Liveness detection

Please provide a clear cost estimation and best practices for optimizing cost.

Thank you.I want to understand the pricing structure of Azure Cognitive Services Face API for my use case.

My use case:

Employee attendance system using face recognition

Around 100–500 employees

Each employee will punch attendance 2 times per day (in/out)

Using Face Detection and Face Identification APIs

I need clarification on:

How many API transactions are counted per punch?

Estimated monthly cost for my usage

Difference in pricing between Face Detection, Identification, and Verification

Whether face storage and training incur additional charges

Cost impact of enabling Face Liveness detection

Please provide a clear cost estimation and best practices for optimizing cost.

Thank you.

Answer 1

Hello @Aryan Sharma

Thank you for your question. Azure AI Vision Face API can be a great fit for an employee attendance solution, and I'd be happy to help you understand the pricing model and expected costs.

Face Identification, Verification, and Liveness Detection are governed by Microsoft’s Responsible AI and Limited Access requirements. You must obtain approval before using these capabilities in production. More information is available at: https://aka.ms/facerecognition.

Typical Attendance Workflow

For each employee check-in or check-out, the standard workflow is:

1. Face Detection – Detect the face in the captured image.
2. Face Identification – Match the detected face against your enrolled employee database.

This means that each attendance punch typically generates:

• 1 Face Detection transaction
• 1 Face Identification transaction

Total: 2 billable transactions per punch

If you also enable anti-spoofing protection:

• Add 1 Face Liveness transaction per punch, or
• Use Face Liveness with Verification as a combined operation.

Estimated Monthly Usage

Assuming:

• Employees: 100 to 500
• Punches per day: 2 (check-in and check-out)
• Working days per month: 22

For 100 Employees

• Total punches: 4,400/month
• Detection calls: 4,400
• Identification calls: 4,400
• Total standard transactions: 8,800/month

For 500 Employees

• Total punches: 22,000/month
• Detection calls: 22,000
• Identification calls: 22,000
• Total standard transactions: 44,000/month

If Liveness is enabled, add an equal number of liveness transactions.

Pricing Model

For standard Face API operations:

• Face Detection
• Face Identification
• Face Verification

Each API call is counted as one transaction. For most pricing tiers, these operations are billed from the same standard transaction pool.

Free Tier

Includes up to 30,000 transactions per month

Suitable for development and initial testing

Identity features still require Responsible AI approval

Standard (S0) Tier

Pay-as-you-go pricing

Recommended for production workloads

Face Storage and Training

Face Storage

Persisted faces stored in a Person Group or Large Person Group are billed separately.

Pricing is based on the average number of stored faces per month.

For your scenario (100–500 employees), storage costs are typically minimal.

Training

Training a Person Group or Large Person Group incurs a small additional charge.

Billing is based on the number of faces trained (metered per 1,000 images processed for large-scale training operations).

Since employee enrollment changes infrequently, training costs are generally negligible.

Liveness Detection Pricing

Liveness is billed separately from standard Face API operations:

• Face Liveness: approximately $15 per 1,000 transactions
• Face Liveness + Verification: approximately $15.50 per 1,000 transactions

Because liveness pricing is significantly higher than standard face operations, it can substantially increase overall costs.

For example, at 500 employees:

• 22,000 punches/month
• Liveness cost alone: approximately $330/month

Estimated Monthly Cost Summary

Without Liveness

100 employees: typically low monthly cost, often within or near the free tier

500 employees: still relatively modest under Standard pricing

With Liveness Enabled

100 employees: approximately $66/month for liveness alone

500 employees: approximately $330/month for liveness alone

Standard detection and identification costs would be additional but are comparatively much lower.

Cost Optimization Best Practices

• Use Detection + Identification as the default attendance workflow.
• Enable Liveness Detection only if anti-spoofing is a business requirement.
• Store one or two high-quality enrollment images per employee.
• Retrain the Person Group only when adding or updating employees.
• Remove inactive employees from the Person Group.
• Avoid requesting unnecessary face attributes, as they add latency without benefiting attendance scenarios.
• Use high-quality image capture to reduce retries and failed detections.

Recommended Architecture

For most attendance systems:

Standard security: Face Detection + Identification

High security: Face Detection + Liveness + Identification or Liveness with Verification

This provides the best balance between cost, performance, and security.

Please refer this

Face API Pricing: https://azure.microsoft.com/pricing/details/cognitive-services/face-api/

Face API overview & operations: https://learn.microsoft.com/azure/ai-services/face/overview-identity

Limited Access & registration (Identification/Verification): https://learn.microsoft.com/azure/cognitive-services/cognitive-services-limited-access

I Hope this helps. Do let me know if you have any further queries.

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Answer 2

Pricing details, per-API transaction costs, and exact monthly estimates for Azure AI Face (Face API) aren't provided in the context, so only structural and functional aspects can be described.

For this scenario (employee attendance using face recognition with 100–500 employees, 2 punches/day, using Detection, Identification, and optionally Liveness), the following applies based on the service behavior in the context:

1. API calls involved per punch

A typical “punch” that uses face recognition generally requires at least:

• One Facial detection call: to answer “Are there one or more human faces in this image?” and to get bounding boxes for faces.
• One Facial identification call: to answer “Can this detected face be matched to any enrolled face in a database?” (one-to-many matching against the employee group).

So, per punch, at least two API operations are involved:

• 1 × Detect
• 1 × Identify

If Facial liveness detection is added, an additional Detect Liveness operation is used to determine whether the face is real and present.

2. Functional differences between Detection, Identification, and Verification

From the context:

• Facial detection
  • Finds human faces in an image and returns bounding boxes.
  • Can optionally return facial attributes (pose, landmarks, etc.).
  • Models are incapable of verifying or identifying individuals and do not create facial templates.
  • All other Face API functions depend on detection first.
  • Reference: Use cases for Azure AI Face service
• Facial identification
  • “One-to-many” matching.
  • Answers: “Can this detected face be matched to any enrolled face in a database?”
  • Returns candidate matches based on similarity between the probe image template and enrolled templates.
  • Used for scenarios like matching an employee’s face against a group of enrolled employees.
• Facial verification
  • “One-to-one” matching.
  • Answers: “Are these two images of the same person?”
  • Compares a probe image template to a single enrolled template.
  • Typical use: comparing a selfie to a stored ID photo.

Functionally, an attendance system with a shared employee gallery normally uses Detection + Identification rather than Verification, unless each punch is explicitly comparing against a single known reference image.

3. Facial liveness detection impact

From the context:

• Facial liveness detection
  • Determines the authenticity of a face and returns a liveness classification (live vs spoof).
  • Models are incapable of verifying or identifying individuals, but may find, extract, or create facial templates to ensure the same person is present during the liveness test.
  • Can be used standalone (e.g., as a CAPTCHA alternative) or combined with verification/identification to improve security.

Adding liveness means:

• One additional liveness operation per punch (if required for every punch).
• Higher overall transaction count compared to using only Detection + Identification.

4. Limited access and eligibility

The Face service is a Limited Access service:

• Access is limited based on eligibility and usage criteria.
• Only available to Microsoft managed customers and partners.
• Use the Face Recognition intake form to apply for access.
• Certain capabilities (for example, some recognition features) are limited to approved use cases.

Note: Facial detection, facial attributes, and facial redaction use cases do not require registration, but identification/verification scenarios do fall under the Limited Access policy.

5. Storage and training

From the context:

• Facial detection models do not find, extract, or create facial templates.
• Facial liveness detection models may find, extract, or create facial templates, but only to ensure the same person is present during the liveness test.

The context does not provide explicit billing details for:

• Storage of enrolled faces/person groups.
• Training or management of those groups.

Therefore, no cost statement can be made about storage/training from the provided information.

6. Cost estimation and optimization

The context does not include any numeric pricing or per-transaction cost, so no concrete monthly cost estimate can be calculated here.

However, cost optimization for this pattern, based on the functional behavior in the context, generally involves:

• Minimizing unnecessary calls:
  • Ensure each punch performs exactly one Detect and one Identify (and one Liveness call only if required).
  • Avoid repeated detection/identification if the same frame is processed multiple times.
• Using the appropriate function:
  • Use Identification for one-to-many employee matching.
  • Use Verification only when explicitly comparing against a single known reference image.
• Using liveness selectively:
  • Apply liveness detection where risk is higher (for example, first punch of the day, or high-value operations) rather than on every single punch, if business requirements allow.

For precise pricing, per-API transaction costs, and a numeric monthly estimate, the Azure pricing calculator and the Face service pricing page must be consulted directly, which are not included in the context.

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References:

• Use cases for Azure AI Face service
• Azure Face API
• Use Face Check with Microsoft Entra Verified ID and unlocking high assurance verifications at scale
• FAQs about AI capabilities in Dynamics 365