Facial Recognition App Using AI and How to Build It in Clappia

Facial recognition is no longer limited to surveillance systems or high-end security devices. With AI-powered image analysis now available through cloud platforms, even business apps can use image-based comparisons to verify identity more easily.

This can be useful in several workflows, from confirming who is filling out a mobile form, to verifying access to equipment or logging site visits. With the right setup, Clappia apps can perform simple facial matching between a stored photo and a newly uploaded one using an AI block.

However, it’s important to note that this capability is enabled through general-purpose AI models (LLMs), not a dedicated facial recognition engine. Some models (like Gemini 1.5 Pro) work reliably for this use case, while others like OpenAI’s GPT or Claude may either refuse to process facial data or produce inconsistent results.

For business users looking for a practical, a no-code approach to identity verification without the complexity of integrating external biometric systems provides a useful and scalable workaround.

To understand how this applies in practice, let’s look at a scenario familiar to many operations teams where this capability becomes especially useful such as in attendance verification for field staff.

In industries such as construction, utilities, logistics, and services, field teams play a critical role in day-to-day operations. Businesses need reliable ways to log attendance, confirm on-site presence, and ensure that the right person is in the right place at the right time. This is important not only for HR purposes but also for operational accuracy, safety compliance, and quality assurance.

Traditionally, such verification depends on:

• Manual attendance sheets
• GPS-only check-ins
• ID card swipes
• Or in some cases, standalone biometric systems

These methods either lack real-time proof, are prone to manipulation, or require expensive hardware infrastructure. So, how do you verify identity in a remote, scalable, mobile-friendly way especially when you’re working across dozens of locations and contractors?

That’s where facial recognition using AI comes in.

And thanks to the latest advancements in AI and image processing, this doesn’t require building a complex system from scratch anymore. You can set up a fully functional facial recognition app right inside a no-code platform like Clappia without needing a separate AI provider, biometric device, or coding knowledge.

Practical Use Case: Attendance Verification with Face Match

Let’s take a common example: field staff attendance.

Let’s say a company manages solar plant installations or generator maintenance across the country. It employs both full-time technicians and on-ground contractors. Every day, employees visit different project sites. But how can the central admin team be sure that the person checking in is actually the one assigned, especially on restricted sites?

A simple facial recognition check within the attendance app helps.

Here’s the core idea:

• Each employee’s official photo is already stored in a master database
  
• When someone marks attendance, they’re required to take a live selfie
  
• An AI model compares the live photo with the stored photo
  
• If the face matches, the submission is marked valid, otherwise it’s flagged.
  
  

This doesn’t eliminate the need for GPS or live tracking. It just adds an extra, AI-powered layer of verification that ensures the data being collected is tied to a real person in real time without needing biometric hardware or custom integrations.

How It Works in Clappia - A Step-by-Step Process

Now let’s look at how you can build this exact workflow in Clappia, a no-code platform for creating apps for internal business processes. Apps built in Clappia are mobile-ready by default, meaning field staff can submit data directly from their phones without any additional setup. Access controls are also built in, so you can restrict app usage to specific users or teams.

To get started, all you need to do is sign up > click ‘New App’ from your dashboard and begin adding the necessary fields. Once your app is published, it can be accessed instantly on both desktop and mobile.

From here, we’ll walk through the steps to implement a simple facial recognition flow using Clappia’s built-in blocks.

Step 1: Create the Master Employee MIS App

Before building the attendance app, you’ll first need a central app to store employee records — essentially your Employee MIS.

Key Fields to Include in this app:

• Employee Name (Single Line Text Block: to capture short text)
  
• Employee ID (Unique ID: to auto generate employee id / Single Line Text block)
  
• Department / Designation (Dropdowns or Single Selector blocks)
  
• Official Email / Contact (Email Block or Phone Number Block)
  
• Photo of the employee (Camera, Image & Files Block)

This app doesn’t need to be filled regularly. It serves as a read-only reference for the actual attendance app. Ensure that the uploaded photos are current, clear, and standardized (frontal view, no face coverings).

Set field validations to ensure that only one submission per employee ID is allowed, preventing duplicate records.

Step 2: Create the Attendance App

This is the app where employees will actually mark their attendance using AI facial recognition. You can add any field you need from the list of pre-coded blocks available in Clappia to build this app as per your workflow. For the AI-powered facial recognition to function, there are three essential blocks that must be included

Must-Have Blocks in the app:

1. Get Data from Other Apps

This block allows you to fetch information from another Clappia app, in this case, the Employee MIS app where the official employee photos are stored.

In the block's set up panel:

• Select the target app as the MIS app
  
• Choose a matching field, typically Employee ID or Email ID, that is unique
  
• Select the Camera block from the MIS app as the field to be fetched.
  
• You can make this field mandatory.

Once configured, this block will automatically insert the fetched camera field (the stored employee image) into the form.

Since this photo is only used internally by the AI for comparison and doesn’t require any user interaction, you can mark this field as ‘Hidden’ using the Advanced tab of that field.

Label this block something like “Select Employee ID” so users clearly understand what it’s doing.

2. Camera, Images & Files Block (Live Upload)

This block is where the employee will upload a live image, typically by taking a selfie at the time of check-in.

• In the set up panel, set the ‘Allowed File Type’ to Images (Camera uploads) to ensure that users cannot upload images from their gallery. This enforces real-time capture.
  
• This field is essential because it gives the AI model a fresh photo to compare against the one fetched from the MIS app (using the Get data block).
  
• You can make this field mandatory.
  
  

3. AI Block

This block performs the face match between the two photos. Here we made use of field variable names to check data in other fields dynamically. To do that simply type @ to pull up a list of fields added to your app and select the right one. It will then be shown in {}.

After the previous two blocks are filled, the AI block processes both images and generates a response based on the prompt you’ve configured.

Ideally, set this block to display only after both camera fields are populated, using Display Conditions in the Advanced tab.

In the set up panel:

• Select your preferred model. Currently, Gemini (with the latest model) is recommended for visual tasks like facial recognition.
• Provide a clear prompt like:
  "Compare the employee's photo uploaded in {camera_ima} and in {camera_ima_1}. If their face in the photo they upload in {camera_ima} matches the one in {camera_ima_1}, mention "Approved" else "Not Approved". No other text."
• You can customize the prompt further depending on your business rules.

You’ll need to give clear instructions in the AI block, specifying how to compare the images and what kind of output to expect.

Not all AI models support facial recognition equally. Based on our internal testing:

• OpenAI’s GPT models often refuse to process facial images due to restrictions in their safety guidelines.
  
  
• Claude (Anthropic) may occasionally produce facial comparison outputs but tends to be inaccurate when there are major changes in facial features (e.g., hairstyle, lighting, accessories) or inconsistent when comparing subtle differences.
  
  
• Gemini (Google), particularly the latest model (e.g., Gemini 1.5 Pro), has shown the most reliable results for face matching tasks in Clappia apps.

We recommend using Gemini and testing with multiple model versions (if available) before finalising your AI Block.

4. (Optional) Display Logic for AI Block

In the right panel > Advanced > “Display this field if”, configure the AI Block to appear only when both camera fields are filled.

This helps prevent users from triggering the AI without first taking the photo or selecting the employee.

5. Other Attendance Fields (as required)

• Date / Time (can be auto-captured)
• Project Site Name
• Notes, Tasks Done, etc.
  
  

6. Optional Field: Match Result Display

To stop users from submitting the form when the facial match fails, you can use a Validation Block.

Add a new Validation Block to your form and in the setup panel:

• Select Custom Validation.
  
• Set the condition as something like {ai_block} = "Not Approved" (replace with your AI block variable name).
  
• Set the level as Error.
  
• Provide a user-facing message like: "Face does not match official record. Please try again".
  
  

This is optional but useful when you want to strictly enforce identity verification before allowing submission.

Handling Mismatches, Errors, and Edge Cases

No facial recognition system is perfect especially when using general-purpose AI models. Differences in lighting, facial angles, expression, or even hairstyle can cause a mismatch even if it’s the right person. So it’s important to design your Clappia app to gracefully handle these edge cases.

Rather than completely blocking submissions when a mismatch occurs, you can build a fallback mechanism using Clappia's workflow automation feature. Workflows allow you to automate actions like sending an email based on conditions defined in the submission.

You can use workflows available in Clappia to set it up using an ‘IF’ node (conditional check) followed by an ‘Email’ node to handle flagged submissions.

• If Node: Check the output of the AI block. For example, if the AI output is "Not Approved", this node acts as a filter and allows the workflow to proceed only in such cases.
  
• Email Node: Add an Email node next. Configure it to notify the relevant team or supervisor with the submission details and a message asking for manual verification. You can include both the uploaded and reference images references in the email body.

This allows your system to capture mismatches, alert the right people, and ensure follow-up happens without blocking submissions entirely.

How the App Works?

Once you’ve built and published the facial recognition app in Clappia, using it is simple for your employees. Here's what the experience looks like from user’s perspective:

1. Accessing the App:
   Field staff or employees can install the Clappia mobile app from the Play Store or App Store. Once logged in, they’ll see the attendance app assigned to them right on their home screen.

2. Filling the Form:
   When the app is opened:
   
   • The user selects their Employee ID from a dropdown (fetched from the MIS app).
   • A hidden field auto-fills their official photo.
   • They are prompted to take a real-time photo using the front camera. Gallery uploads are disabled to ensure live capture.

3. AI Face Match (in Background):
   Once both photos are available, the user can click on the AI button which then runs automatically in the background. It compares the two and displays whether the face matches.

4. Automatic Handling of Mismatches:
   If a mismatch occurs:
   
   • You can optionally prevent submission using the Validation Block.
   • Alternatively, if submissions are allowed, a Workflow is triggered in the background. This immediately sends an email to a manager or admin with the submitted photos and a request for verification.

All of this happens without any manual intervention, giving users a smooth experience while ensuring that mismatches are still flagged and managed automatically.

Tips for Best Results

Facial recognition in Clappia depends on third-party LLMs like Gemini, which operate via text + image prompts. While this is sufficient for general matching, it has some important limitations:

• No facial embeddings or biometric templates are created (as would be the case in a dedicated facial recognition engine).
  
• Accuracy can drop if:
  
  • The uploaded image is too dark, blurry, or angled
  • The reference photo is outdated
    
• Cannot detect liveness (i.e., whether a photo is of a real person or a printed image), but this could be avoided by giving more instructions to the AI..
  

Tips:

1. Use Recent and Clear Profile Photos in the MIS app.
   
2. Instruct Employees to take selfies in good lighting, directly facing the camera.
   
3. Disable Gallery Uploads using the “Camera Upload Only” option to prevent tampering.
   
4. Consider Re-validating Reference Images every few months.
   
5. Set Up Display Conditions to ensure the AI block is triggered only after all required inputs are filled.

Add Backup Fields in the App to Handle Mismatches

AI-powered face matching is not always 100% reliable especially in low-light conditions, motion blur, or when network latency causes delays in photo uploads. Instead of blocking the user entirely or routing everything to admin teams, you can build additional input fields into your form as a fallback option.

Here are some suggestions:

• Dropdown (Discrepancy Reason)
  Let the user explain why the AI result might be inaccurate. Example options:
  
  • "Photo didn’t upload properly"
  • "Lighting was poor"
  • "Could not take a clear selfie"
  • "Face partially covered (mask, helmet, etc.)"
    
• Remarks / Reason Text Field
  A free-text field where the user can add a short explanation.
  
• Location Verification
  
  • GPS Location Block to capture real-time coordinates
  • Geofencing can be configured to ensure the person is within a designated site boundary.
    
• Live Tracking (Optional)
  For field staff or on-ground contractors, enabling Clappia’s Live Tracking feature during submission adds another layer of presence verification.
  
• Employee ID or Supervisor Approval Field
  
  • If needed, request a manual entry of some employee ID, verification number, etc.
  • Or allow a supervisor to review and approve the submission later (via a linked workflow).

These additions won’t replace AI verification but will help the admin or HR team make informed decisions in edge cases

Other Use Cases for Facial Recognition in Clappia

While this guide focused on attendance, facial recognition using AI in Clappia can support a wide range of scenarios:

1. Gate Entry Validation
   Cross-check visitor faces with a pre-uploaded whitelist (e.g., for events or restricted areas)
   
2. Asset Handover Tracking
   Match a technician’s face before handing over expensive equipment or tools.
   
3. Quality Checks in Manufacturing
   Validate whether the assigned inspector is the one performing checks at a given station.
   
4. Customer KYC
   Capture live images of customers and compare against uploaded ID photos.
   
5. Remote Exam Proctoring
   Match student identity from records before starting a test or assessment.
   
   

Each of these use cases can be implemented by adjusting the source app (MIS) and AI prompt while following the same underlying logic.

Bringing AI Results into Dashboards and Reports

Once the data is collected, Clappia allows you to visualise and track facial recognition results over time.

You can:

• View match/mismatch counts by employee, department, or site
  
• Add filters to view only submissions marked “No Match”
  
• Create dynamic dashboards and embed them inside the Clappia app homepage
  
• Export the AI results to Google Sheets and build external BI dashboards (e.g., in Power BI)

This gives teams not only real-time validation but also visibility into how effective the facial recognition is across locations.

Security, Privacy, and Access Control

Since facial data is involved, make sure to:

• Restrict access to both the Employee MIS app and attendance submissions.
  
• Limit AI results to Admin view only if necessary.
  
• Avoid exposing reference photos to regular users by keeping them hidden in the app.
  
• User permissions help to control who can view or edit past records.
  
• Follow internal company policies on photo collection and consent.
  
  

Clappia doesn’t store or train any AI models on the photos. All face matching is done via API calls to third-party models, and the photos are retained only in your own app records.

A Practical, Scalable Way to Add Identity Verification

Facial recognition, once reserved for high-end systems, is now practical even in internal business workflows especially with tools like Clappia. You don’t need to invest in biometric hardware or hire developers. With just a few blocks and the right prompt, you can build a fully functioning identity verification system inside your business apps.

This approach is especially useful for field teams, contractors, or remote employees where trust, traceability, and accountability matter. Whether it’s for attendance, asset handovers, inspections, or restricted access, AI-based face matching adds a layer of assurance.

It’s not meant to replace biometric-grade systems but to give your team a simple, mobile-ready fallback that works with what you already have.

If you’re managing distributed teams or sensitive workflows, this might be the easiest way to introduce smart verification with an app, in just a few clicks.

Frequently Asked Questions (FAQ)

1. How does Clappia do facial recognition without a biometric system?
Clappia uses general-purpose AI models (LLMs) to compare images, rather than a dedicated biometric engine. This allows you to set up facial recognition workflows without coding or external integrations.

2. How does the app work for end users?
Once the app is assigned to a user, they can log into the Clappia mobile app, open the app assigned to them and simply fill out required details and take a live photo (selfie) using the camera. All facial comparison and logic runs in the background.

3. How reliable is facial recognition using AI?
It depends on the AI model. Gemini (latest versions) provides the most consistent results. OpenAI models often reject facial data. Claude sometimes misses facial differences or fails with major appearance changes.

4. Can it detect fake photos or printouts?
It can, but there could be inaccuracies at times. These models aren’t designed for spoof detection, so accuracy may vary. It’s best to encourage users to upload a live, front-facing selfie in natural lighting.

5. Does this work offline?
No. The AI Block requires a live internet connection to process inputs through cloud-based LLMs.

6. Is the data secure?
Yes. Photos are stored in your app’s database and not accessed by Clappia. Image comparison happens via secure API calls to AI providers. Clappia does not retain any data from the AI output.

7. What if the AI block says that there is a mismatch in facial recognition?
You have several fallback options you can include in your app:

• Prevent submission using a Validation block
• Trigger an Email workflow for admin review
• Add extra fields:
  
  • A dropdown asking “Was this a mismatch?”
  • A text field for “Reason” (e.g., poor lighting, night-time)
  • Location capture, live tracking, or alternate ID fields
    These fields help add context and allow the business to log edge cases.

8. Can I include GPS or ID checks too?
Yes. You can combine facial recognition with GPS & geofencing, Live tracking, Manual ID inputs, approvals, etc. These are all standard Clappia blocks and can be added easily to the app.