What Is a Face Detection System?
Direct answer: A face detection system is a computer vision application that uses AI models to identify whether human faces are present in an image or video frame, and to determine their location — typically expressed as a bounding box and a confidence score.
Face detection does not, on its own, tell you who a person is. It answers a narrower and more foundational question: is there a face here, and where exactly does it sit in the frame? That output — face presence and face location — is what downstream systems act on.
At a conceptual level, the flow looks like this:
Image / Video Input → Face Detection Model → Face Location → Bounding Box → Confidence Score → Application Action
Key building blocks of a face detection system include:
It is worth stating this plainly because it shapes both the engineering approach and the privacy posture of the system: face detection does not inherently determine a person's identity. Identity requires a separate, additional layer of technology — face recognition — which is discussed later on this page.
Face Detection vs. Face Recognition
This is the single most important distinction on this page, and it is central to how we scope every face detection engagement.
Face Detection answers: "Is there a face, and where is it?"
Face Recognition answers: "Whose face is it?"
| Face Detection | Face Recognition |
|---|---|
| Detects presence and location of faces | Identifies or verifies a specific person's identity |
| Answers "Is there a face?" | Answers "Whose face is it?" |
| Outputs face locations (bounding boxes) | Outputs an identity match or verification result |
| Confidence score reflects detection certainty | Confidence score reflects identity-match certainty |
| Does not inherently identify a person | Explicitly designed for identity-related tasks |
| Frequently used as an input to downstream systems | Used specifically for identity verification or lookup |
Choosing the right technology matters commercially as well as technically. A business that only needs to know whether a face is present — for example, to trigger a camera event, count visitors, or confirm a person is looking at a kiosk — does not need facial recognition, and should not build or license identity-matching capability it does not require. Scoping the project correctly reduces cost, reduces privacy exposure, and reduces regulatory obligations.
InfinitetechAI designs systems that match the actual capability required. Where recognition is genuinely needed for a use case, we scope it as a distinct, additional layer built on top of a face detection foundation — never conflated with detection by default.
Face Detection Services
InfinitetechAI provides end-to-end face detection engineering across the following service areas. Each is scoped around a specific business problem and a specific detection approach.
Custom Face Detection
Business problem: Off-the-shelf detectors rarely match a specific camera environment, lighting condition, or accuracy requirement out of the box. Detection approach: A detection pipeline selected, configured, and where necessary fine-tuned around your actual deployment conditions. Typical application: Proprietary applications, specialized environments, unusual camera angles. Business value: Detection performance tuned to your real-world conditions, not a generic benchmark.
Real-Time Face Detection
Business problem: Applications that need to react to a face the moment it appears — not after a batch delay. Detection approach: Low-latency inference pipelines optimized for frame-rate throughput. Typical application: Kiosks, smart cameras, interactive displays, live video feeds. Business value: Immediate application response, better user experience.
Image Face Detection
Business problem: Static images — uploads, photos, batches — need faces located for downstream processing. Detection approach: Batch or on-demand detection across still images. Typical application: Photo organization, image moderation pipelines, content workflows. Business value: Automated, consistent face localization across large image sets.
Video Face Detection
Business problem: Recorded or live video needs faces detected frame by frame. Detection approach: Frame-level detection combined with temporal consistency logic. Typical application: CCTV review workflows, video analytics inputs, media processing. Business value: Structured, searchable face-location data extracted from video.
Multi-Face Detection
Business problem: A single frame may contain several people, at different sizes and angles. Detection approach: Detection tuned to return every face above a confidence threshold, not just the most prominent one. Typical application: Group photos, classrooms, events, retail floors, meeting rooms. Business value: Complete, not partial, visibility into everyone present in a frame.
Face Localization
Business problem: Downstream systems need precise coordinates, not just a yes/no answer. Detection approach: Bounding-box output with coordinates and confidence per detection. Typical application: Cropping, UI overlays, tracking pipelines, region-of-interest processing. Business value: Structured spatial data that other systems can consume directly.
Face Tracking
Business problem: Detecting a face in one frame is not the same as following that face across a video. Detection approach: Detections are associated across consecutive frames to maintain continuity. Typical application: Occupancy counting, dwell-time analysis, camera-based interaction. Business value: Continuity of detection through movement, brief occlusion, and camera motion.
Face Detection APIs
Business problem: Other teams and applications need to consume detection capability without owning the model. Detection approach: A documented API layer exposing detection, localization, and confidence data. Typical application: Internal platforms, partner integrations, multi-application ecosystems. Business value: Reusable detection capability across your product portfolio.
Mobile Face Detection
Business problem: Mobile apps often need on-device or low-latency detection with constrained hardware. Detection approach: Lightweight models suited to mobile CPUs/GPUs, or an efficient backend architecture. Typical application: Camera apps, mobile kiosks, field applications. Business value: Responsive detection without unnecessary battery or bandwidth cost.
Web Face Detection
Business problem: Browser-based applications need detection without a heavy native install. Detection approach: Web-compatible detection, either client-side or via API calls to a backend. Typical application: Web portals, browser-based check-in, interactive web experiences. Business value: Cross-platform detection reachable from any modern browser.
Edge Face Detection
Business problem: Some environments cannot or should not send every frame to the cloud. Detection approach: Detection models optimized to run on local edge hardware. Typical application: Bandwidth-constrained sites, privacy-sensitive deployments, latency-critical settings. Business value: Local processing that reduces bandwidth dependency and central data exposure.
Camera-Based Face Detection
Business problem: Physical camera deployments — fixed, PTZ, or smart cameras — need detection wired into the video pipeline. Detection approach: Integration between camera streams (RTSP/ONVIF or vendor SDKs) and the detection engine. Typical application: Smart buildings, retail floors, entry points, event venues. Business value: A working pipeline from physical camera to actionable detection event. Discuss Your Face Detection Use Case
Real-Time Face Detection
Real-time face detection is a distinct engineering problem from processing a static image. A static image can be processed at whatever speed is convenient. A live camera feed, a webcam, or a mobile stream cannot — the system has to keep up with incoming frames, or it falls behind and the application feels sluggish or unresponsive.
Real-time face detection is used across a wide range of environments:
Engineering considerations that shape a real-time system include:
A well-engineered real-time pipeline balances these factors deliberately, rather than simply pointing a general-purpose detector at a live feed and hoping the frame rate holds up.
CCTV and security video feeds
Webcams in desktop and web applications
Mobile device cameras
Smart cameras in retail and public spaces
Interactive kiosks and self-service terminals
Event and venue camera systems
Live interactive application interfaces
Latency — how quickly a detection result needs to be available after a frame is captured
Frame processing rate — how many frames per second the pipeline must sustain
Detection confidence thresholds — tuned differently for real-time responsiveness versus offline accuracy
Handling of multiple faces appearing and leaving the frame
Variable camera conditions — lighting changes, motion, distance from camera
Processing constraints — available compute, GPU access, and whether inference runs centrally or at the edge
Video Face Detection
Video introduces a temporal dimension that static images do not have. Instead of detecting faces in a single frame, a video face detection system processes a sequence of frames and, where appropriate, links detections across them.
Frame → Face Detection → Tracking → Application Event
Typical video face detection scenarios include:
It is worth noting explicitly what this page does not cover: general video analytics such as object counting, motion detection unrelated to faces, or broad activity recognition. Those capabilities sit within a broader computer vision scope, not within face detection specifically.
Recorded video review, where detections are extracted after the fact
Live video streams, where detections are generated as frames arrive
Camera streams feeding into a broader monitoring or event pipeline
CCTV feeds where face presence is one signal among several
Multi-frame detection used as an input to face tracking
Face Localization and Bounding Boxes
Direct answer: Face localization is the process of determining precisely where a detected face sits within an image, typically expressed as a bounding box — a rectangular region defined by coordinates.
Localization output typically includes:
Precise localization matters because it is what downstream systems actually consume. Applications rely on accurate face coordinates for:
Bounding boxes — the rectangular region containing each detected face
Coordinates — the position of that region within the frame
Face regions — the pixel area corresponding to the detected face
Confidence scores — the certainty associated with each localized region
Multiple detected regions — where more than one face is present
Cropping images to a face region
Feeding tracking systems with a starting point for each detected face
Driving further downstream processing where a face region is the input
Positioning UI elements — overlays, indicators, or interactive controls — relative to a detected face
Structuring visual workflows that depend on knowing exactly where in the frame a face is located
Face Detection Features
Depending on scope, a InfinitetechAI face detection system can include:
Not every engagement requires every feature. Feature scope is set during the discovery phase, based on your camera environment, application, and business requirement — not applied as a fixed package.
Face localization with bounding-box output
Configurable confidence scoring and thresholds
Multi-face detection within a single frame
Real-time inference tuned to your latency requirements
Face tracking across video frames
Image preprocessing for lighting and quality variation
Handling of pose variation — angled and partially turned faces
Handling of lighting variation across indoor, outdoor, and mixed environments
Handling of partial occlusion
Support across a range of input resolutions
Integration with physical camera systems
API-based integration for internal or partner applications
Mobile deployment options
Web deployment options
Edge inference where hardware and model choice support it
Face Detection Use Cases
Face detection technology provides immense value across various sectors. Here is how different industries are utilizing our solutions to enhance security and operational efficiency.
Access-System Interfaces
Problem: A physical or digital access point needs to know a person is present before triggering a workflow.
Face Detection Solution: Face detection confirms presence and location at the point of interaction.
Business Value: Reduced manual monitoring, faster interaction triggers.
Attendance Applications
Problem: An attendance workflow needs to detect a face in the camera frame as a first step.
Face Detection Solution: Face detection identifies the face region; any separate identity step is handled independently.
Business Value: Faster, more consistent capture of attendance-relevant frames.
Smart Cameras
Problem: A smart camera product needs to react when a person is in view.
Face Detection Solution: Face detection runs on or near the camera to trigger downstream logic.
Business Value: Differentiated product capability, reduced false triggers from non-human motion.
Retail Applications
Problem: A retail environment wants a presence signal without building identity infrastructure.
Face Detection Solution: Face detection acts as a visual presence signal for downstream analytics.
Business Value: Better understanding of visitor presence without unnecessary data collection.
Customer Experience Systems
Problem: An interactive display should respond when someone approaches.
Face Detection Solution: Real-time face detection triggers the interface response.
Business Value: More engaging, responsive customer-facing technology.
Photo Organization
Problem: A photo platform needs to group or tag images based on face presence.
Face Detection Solution: Batch face detection locates faces across a photo library.
Business Value: Automated organization at scale.
Video Applications
Problem: A video platform needs structured metadata about where faces appear.
Face Detection Solution: Frame-level face detection generates location and timing metadata.
Business Value: Searchable, structured video content.
Security-Oriented Detection Applications
Problem: A monitoring workflow needs to know when and where faces appear in a feed.
Face Detection Solution: Real-time or near-real-time face detection flags relevant frames.
Business Value: Reduced manual video review workload.
Hospitality Applications
Problem: A hotel or venue wants to streamline a check-in or service interaction.
Face Detection Solution: Face detection identifies presence at a kiosk or counter camera.
Business Value: Smoother, faster guest-facing interactions.
Event Applications
Problem: An event platform needs to detect multiple faces across images or video.
Face Detection Solution: Multi-face detection processes crowd and group scenes.
Business Value: Better structured data from event photography and video.
Interactive Kiosks
Problem: A kiosk should activate or adjust its interface when a person steps up.
Face Detection Solution: Real-time face detection triggers the kiosk's active state.
Business Value: Improved usability, reduced idle-state confusion.
Smart-Building Applications
Problem: A building system wants occupancy-aware behavior without full identity systems.
Face Detection Solution: Face detection contributes a presence signal to broader building logic.
Business Value: More responsive, efficient building operations.
Mobile Applications
Problem: A mobile app needs real-time face localization from the device camera.
Face Detection Solution: A lightweight on-device or backend-assisted detection model processes frames.
Business Value: Responsive in-app camera experiences.
Web Applications
Problem: A browser-based product needs face detection without native installation.
Face Detection Solution: Web-compatible detection, client-side or via API.
Business Value: Broad reach across devices without extra installs.
Face Detection Across Industries
Across every industry, the emphasis stays on detection and localization — face presence, count, and position — rather than biometric identification, unless a client has an explicit, separately scoped identity requirement.
Retail: Presence detection at entrances and displays, footfall-related signals, and interactive in-store technology — all without building an identity layer that isn't required.
Education: Camera-based presence detection at classroom or campus entry points, feeding into broader attendance or facility workflows.
Healthcare: Presence-aware interfaces in waiting areas and reception points, handled with particular care given the sensitivity of healthcare environments.
Hospitality: Check-in kiosks, guest-facing interactive displays, and presence-triggered service workflows.
Banking: Presence detection at self-service terminals and branch entry points, kept deliberately separate from any identity verification layer.
Security: Camera-based face detection as one input among several within a broader monitoring workflow.
Events: Multi-face detection across crowd and group scenes for event technology and media applications.
Automotive: In-cabin camera systems that detect driver or occupant presence for safety-related features.
Smart Buildings: Occupancy-aware building systems that use face presence as one signal for space and energy management.
Consumer Applications: Camera-based features in consumer apps — filters, framing assistance, presence-triggered UI.
Entertainment: Interactive installations and experiences that respond to detected faces in real time.
Face Detection Technology Stack
InfinitetechAI builds face detection systems using a technology stack matched to the specific requirement. Depending on the project, this can include:
React Native
Node.js
Python
AWS
PostgreSQL
Docker
Kotlin
Swift
React Native
Node.js
Python
AWS
PostgreSQL
Docker
Kotlin
Swift
Face Detection Accuracy and Performance
Face detection performance is not a single fixed number — it depends heavily on deployment conditions. Factors that materially affect detection performance include:
Detection performance ultimately depends on model selection, the quality of the input images, actual camera conditions in your environment, typical face size in frame, the deployment environment (cloud vs. edge), the characteristics of any data used for tuning, and the strictness of your real-time requirements.
We do not promise a universal accuracy figure, because none is honest. Instead, we test against your actual conditions and report what the system achieves in your environment.
Lighting — both very low light and strong backlighting reduce reliability
Face angle — frontal faces are easier to detect than extreme profile views
Camera quality — sensor quality and optics affect the input the model receives
Resolution — very low-resolution frames make small faces harder to detect
Distance from camera — faces far from the camera occupy fewer pixels and are harder to detect
Occlusion — masks, hands, hats, or other faces partially blocking a face
Background complexity — cluttered scenes can increase false positives
Multiple faces — density and overlap in crowded frames
Motion — fast-moving subjects can introduce blur
Frame rate — lower frame rates can miss fast transitions
Image quality — compression artifacts and noise reduce model confidence
Camera placement — angle and mounting height affect how faces appear in frame
Face Detection Challenges and Solutions
No face detection system eliminates every challenge completely. The realistic goal is to understand the challenges specific to your environment and engineer around them deliberately.
| Challenge | Potential Solution |
|---|---|
| Poor lighting | Better image capture, preprocessing, and appropriate model selection |
| Low resolution | Camera/input quality improvements and a suitable model strategy |
| Side profiles | Models and test data representative of realistic pose variation |
| Occlusion | Diverse training and testing examples covering partial visibility |
| Small faces | Appropriate input resolution and detection strategy for distant subjects |
| Multiple faces | Multi-face detection configuration and dedicated evaluation |
| Motion blur | Camera and frame-rate optimization, plus appropriate processing |
| Camera variation | Deployment-specific testing across your actual camera fleet |
| Processing latency | Efficient inference architecture matched to your throughput needs |
| Edge resource constraints | Model and hardware optimization for the target device |
We do not claim these challenges can always be fully eliminated. The commitment is to identify which ones matter most for your environment and engineer the system to handle them as reliably as the underlying technology allows.
Face Detection Security Considerations
Beyond privacy policy, a face detection system needs sound technical security. Areas we address include:
API authentication — ensuring only authorized applications can call detection endpoints
Access control — role- and permission-based access to detection data and configuration
Secure image transfer — protecting frames and images in transit
Encryption where applicable — for stored images and detection metadata
Data retention — defined, enforced limits on how long data is kept
Secure storage — infrastructure hardening for any stored detection-related data
Monitoring — visibility into system health, usage, and anomalies
Abuse prevention — rate limiting and controls against misuse of detection APIs
Model and API security — protecting the detection service itself from tampering or unauthorized use
Infrastructure security — securing the cloud or edge infrastructure the system runs on
Face Detection ROI and Business Impact
Face detection typically creates business value by automating a visual task that would otherwise require manual attention or slower processing. Commonly cited benefits include:
To measure the actual impact in your environment, it helps to track relevant KPIs before and after deployment:
We recommend establishing a clear baseline for these metrics before deployment, so that any improvement can be measured against your own starting point rather than a generic industry claim. We do not publish fabricated ROI percentages, because genuine ROI depends entirely on your specific operating environment.
Faster visual processing of images and video
Reduced manual monitoring effort
Real-time detection enabling immediate application response
Improved responsiveness of camera-driven applications
Automated visual workflows that previously required human review
Better operational efficiency in camera-heavy environments
Increased capacity to process visual data at scale
Faster triggering of downstream events based on face presence
Improved user experiences in camera-driven interfaces
Detection latency
Frames processed per second
Manual monitoring hours before versus after deployment
Number of streams or images processed
Detection review workload for human teams
False-positive rate
False-negative rate
Processing cost per stream or per image
Face Detection vs. Computer Vision
Computer vision is the broader field; face detection is one specialized capability within it.
| Factor | Face Detection | Computer Vision |
|---|---|---|
| Scope | Human face detection | Broad visual AI |
| Primary goal | Detect and localize faces | Interpret visual information generally |
| Applications | Face detection, tracking, camera workflows | Detection, classification, segmentation, OCR, video analytics, and more |
If your project spans multiple visual AI capabilities — for instance, detection combined with classification, segmentation, or OCR — that broader scope is best addressed through our general
If your project spans multiple visual AI capabilities — for instance, detection combined with classification, segmentation, or OCR — that broader scope is best addressed through our general Computer Vision development services.
Face Detection vs. Face Recognition Technology Stack
It helps to see the two pipelines side by side, because face recognition is technically an extension of face detection, not a replacement for it.
React Native
Node.js
Python
AWS
PostgreSQL
Docker
Kotlin
Swift
React Native
Node.js
Python
AWS
PostgreSQL
Docker
Kotlin
Swift
Illustrative Face Detection Use Cases
The following scenarios are illustrative examples used to explain how face detection applies in practice. They are not descriptions of actual client engagements.
Smart Camera Face Detection
Illustrative Use Case
Problem: A smart-camera application needs to detect whether people are present in its field of view.
Solution: The system detects human faces in incoming video frames in real time.
Workflow: Camera → Face Detection → Bounding Box → Event Logic → Application Response
Attendance Application
Illustrative Use Case
Problem: An attendance workflow needs to detect a face within a camera frame.
Solution: Face detection identifies face regions before any separate, additional identity-related processing takes place.
Important: face detection alone does not claim to identify a person's identity. Any identity step is a distinct, separately scoped capability.
Event Face Detection
Illustrative Use Case
Problem: An event application needs to detect multiple faces across images or video.
Solution: Multi-face detection identifies face locations within each frame, even in crowded scenes.
Retail Camera Detection
Illustrative Use Case
Problem: A retail application needs to detect customer presence without building an identity system.
Solution: Face detection acts as a visual presence signal feeding into broader retail analytics.
Mobile Face Detection
Illustrative Use Case
Problem: A mobile application needs real-time face localization from the device camera.
Solution: A lightweight face detection model processes camera frames on-device, or through an appropriately architected backend, depending on the device's capability.
Future of Face Detection
Established Capabilities
Real-time detection of faces in images and video Multi-face detection in group and crowd scenarios Face localization with bounding boxes and confidence scores Face tracking across video frames Cloud and edge deployment of detection pipelines
Emerging Capabilities
More computationally efficient detection models suited to constrained edge hardware Broader on-device inference across a wider range of mobile and embedded hardware Improved robustness to challenging conditions such as extreme lighting or heavy occlusion Greater integration between vision and language models for richer application logic Continued progress on privacy-preserving processing techniques More natural human-AI visual interfaces built on top of reliable detection We are careful to distinguish what is established, production-ready technology today from what remains an emerging or evolving area — and we do not present emerging capabilities as mature, universally available technology.
People Also Ask
What is a face detection system?
A face detection system is a computer vision application that identifies whether human faces are present in an image or video frame and locates them, typically using a bounding box and a confidence score.
How does face detection work?
It works by acquiring an image or video frame, preprocessing it, running it through a face detection model, and outputting the location and confidence of each detected face.
What is the difference between face detection and face recognition?
Face detection determines whether and where a face is present. Face recognition goes further, attempting to identify or verify who that person is.
Can AI detect multiple faces?
Yes. Multi-face detection is a standard capability of a well-engineered face detection system, returning a bounding box and confidence score for each face found in a frame.
Can face detection work in real time?
Yes, when the pipeline is engineered for low latency and sufficient frame-processing throughput, face detection can operate on live camera or video feeds in real time.
Can face detection work with CCTV?
Yes. Face detection can be integrated with CCTV and other video feeds to detect and locate faces frame by frame, subject to camera quality and placement.
Can face detection work on mobile devices?
Yes. Mobile face detection can run on-device using lightweight models, or via an appropriately architected backend, depending on the device's processing capability.
What is face localization?
Face localization is the process of determining exactly where a detected face sits within an image, typically expressed as a bounding box with coordinates.
What is face tracking?
Face tracking associates face detections across consecutive video frames, allowing a system to follow the same face over time rather than treating each frame independently.
How accurate is face detection?
Accuracy depends on real deployment conditions, including lighting, camera quality, face size, and occlusion. There is no universal accuracy figure; performance should be evaluated against your actual environment.
How much does a face detection system cost?
Cost depends on factors such as the number of cameras, real-time requirements, deployment model, and integration scope. There is no fixed universal price.
Can face detection be integrated into an existing application?
Yes. Face detection is commonly exposed through an API so existing applications, both web and mobile, can consume detection results without rebuilding the underlying model.
Can face detection run on edge devices?
Yes, where the model and hardware are appropriately matched. Edge deployment is often chosen to reduce latency and bandwidth use, or to keep processing local for privacy reasons.
Does face detection identify a person's identity?
No. Face detection identifies presence and location only. Identifying who a person is requires a separate technology layer known as face recognition.
Face Detection FAQs
How long does a face detection development project typically take?
Timelines depend on scope — a proof of concept is generally faster to deliver than a full production integration across multiple cameras and applications. InfinitetechAI provides a realistic timeline once your requirement is scoped.
Do we need our own cameras before starting a project?
Not necessarily for a proof of concept, but production-quality testing benefits from access to representative cameras or footage from your actual environment as early as possible.
Can you improve the accuracy of an existing face detection system we already built?
Yes. Optimization engagements review your current model, pipeline, and deployment conditions, and identify where accuracy, latency, or reliability can realistically be improved.
Do you support integration with our existing camera infrastructure?
Camera integration is scoped based on your existing hardware, protocols, and network setup. We assess feasibility during the discovery phase rather than assuming universal compatibility.
Can face detection run without sending video to the cloud?
Edge deployment is an option where the hardware and model support it, allowing processing to happen locally rather than sending every frame to a central server.
Do you provide APIs for face detection?
Yes. Face detection capability can be exposed through a documented API so your engineering teams, or partner applications, can consume detection results directly.
Can the system detect faces in low light?
Detection performance in low light depends on camera quality and preprocessing. We assess your specific lighting conditions during discovery and design the pipeline accordingly, rather than promising universal low-light performance.
Is face detection the same as facial recognition?
No. Face detection identifies presence and location. Facial recognition adds an additional, separate layer of identity-related processing on top of detection.
What happens to images processed by the system?
Data handling — including whether images are stored, for how long, and where — is defined during the project scoping phase based on your privacy requirements and applicable regulations.
Can face detection be deployed on mobile apps?
Yes. Mobile deployment options include on-device lightweight models or backend-assisted architectures, depending on device capability and latency requirements.
Can the system handle multiple video streams at once?
Yes, provided the architecture and infrastructure are scoped for that concurrency from the start. Stream count is a key input into architecture and cost planning.
Do you offer ongoing support after deployment?
Yes. Long-term optimization and monitoring engagements are available for organizations that want continued performance tuning after go-live.
Will InfinitetechAI recommend facial recognition if we only need detection?
No. We scope engagements to the actual technology required. If your use case only calls for face detection, we will not propose a facial recognition layer you do not need.
Can the system be tested on our data before we commit to production?
Yes. A proof-of-concept phase is generally recommended so performance can be evaluated against your own representative data before a full production build.
How do you handle enterprise-scale deployments?
Enterprise deployments are handled through a structured process covering architecture planning, phased rollout, integration, monitoring, and ongoing optimization across your camera and application estate.
Start Your Face Detection Project
If you are evaluating a face detection system for your product, cameras, or enterprise workflow, we are glad to walk through your use case, camera environment, and integration requirements.
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Talk to a Face Detection Expert
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Assess your camera and input requirements
Evaluate technical and commercial feasibility
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Explore a proof of concept on your own data
Discuss API and application integration
Plan cloud or edge deployment
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