Candidate face continuity
94%
face visible
Face Detection Proctoring
Monitor candidate presence with face detection, session evidence, and responsible review.
Protect remote assessments with CloudTest Face Detection Proctoring through candidate face-presence monitoring, identity continuity checks, multiple-person detection, temporary face-absence tracking, webcam visibility signals, camera interruption logs, timestamped session evidence, configurable review rules, AI-assisted event analysis, and detailed proctoring reports for recruitment tests, academic examinations, certification programmes, campus hiring, and online technical assessments.
Face-presence monitoring
Multiple-person signals
Camera interruption logs
Human evidence review
CAM
remote-assessment / candidate-session / face-monitoring-active
Face detected
One candidate currently visible
Candidate
Not found
Not found
10:04
Face present
Clear
10:22
Brief absence
Review
10:23
Face returned
Logged
Face-detection evidence
Candidate visibility, face presence, temporary absence,
multiple-person signals, camera continuity, timestamps,
duration, repeated patterns, technical context, and reviewer
notes
Face monitoring checkpoints
Connect candidate identity, visibility, continuity, and review
Create a structured monitoring process that starts with candidate verification, confirms camera readiness, tracks face presence during the assessment, identifies selected visibility events, and generates a chronological evidence record.
Evaluate the full session instead of judging a single image or isolated event.
Reliable review should consider face visibility, absence duration, repeated patterns, camera interruptions, multiple-person signals, candidate explanations, environmental conditions, approved accommodations, and assessment policy.
Candidate session
Identity and face continuity evidence
Candidate verification
Confirm assessment access, identity process, attempt, consent, and equipment readiness.
Face-presence monitoring
Track whether a candidate face remains visible throughout the configured assessment session.
Multiple-person signals
Log configured events where an additional person or face may be visible in the camera frame.
Contextual human review
Examine event duration, repetition, technical conditions, explanations, policy, and complete evidence.
Detection journey
Follow face detection from assessment entry to reviewed completion
Prepare the candidate, test the camera, confirm face visibility, monitor configured face events, preserve timestamps, and support a responsible final review.
Confirm candidate
Validate access, identity, attempt, consent, and assessment instructions.
Check camera setup
Confirm camera permission, framing, lighting, visibility, and connection.
Detect face presence
Record configured face-visible, face-absent, and visibility changes.
Organize session events
Connect event type, timestamp, duration, frequency, and nearby activity.
Complete review
Consider the full evidence record, candidate context, and assessment policy.
Live face monitoring studio
Monitor candidate visibility without interrupting the assessment
Capture configured face-presence, temporary absence, multiple-person, camera interruption, visibility, and session-continuity signals while the candidate continues working through the online test.
Convert face signals into chronological and reviewable evidence.
AI-assisted detection can help organize events by type, timestamp, duration, recurrence, and configured review priority. An individual event should not automatically be treated as proof of misconduct.
FACE
Records configured face-present and face-absent events
MULTI
Logs selected multiple-person or additional-face signals
CAM
Preserves camera interruptions and restoration timestamps
REV
Groups selected events for authorized contextual review
Face detection signal library
Configure the face and camera evidence appropriate for each test
Select candidate presence, multiple-person, temporary absence, visibility, camera interruption, identity continuity, environment, timestamp, duration, and reviewer-note signals according to your assessment policy.
Presence
Candidate face-presence monitoring
Record whether a candidate face remains visible during the configured assessment period and connect visibility changes with timestamps and duration.
Absence
Temporary face-absence detection
Log selected events where the candidate temporarily leaves the camera frame, becomes obstructed, or is no longer visible.
Person count
Multiple-person detection signals
Surface configured moments where more than one face or person may appear in the candidate's assessment environment.
Continuity
Camera interruption and restoration logs
Preserve camera permission changes, stream interruptions, reconnection events, visibility loss, and recovery timestamps.
Identity
Candidate identity-continuity evidence
Connect configured identity checks with face-presence events and session continuity for authorized review.
Review
Timestamped event and reviewer evidence
Organize event type, time, duration, recurrence, technical context, candidate explanation, reviewer comments, and final decision history.
Face evidence review ribbon
Review face-detection events with their complete session context
Examine candidate verification, face-presence changes, multiple-person signals, camera interruptions, event duration, repetition, nearby assessment activity, candidate explanation, and reviewer notes before recording a decision.
REV
Candidate face-detection evidence timeline
Human review
The session contains two brief face-absence events, one additional-person signal, and one camera interruption.
The reviewer can examine duration, recurrence, environmental context, nearby assessment activity, candidate explanation, technical conditions, and applicable policy.
Candidate verification completed
Clear
Assessment access, camera, face visibility, system, and consent checks were completed.
Face absent for eleven seconds
Review
The candidate returned to the camera frame and continued the assessment.
Brief additional-person signal
Context
A second face-like signal appeared near the edge of the frame for a short duration.
Camera stream interrupted and restored
Technical
The stream resumed automatically after a short connectivity interruption.
Assessment submitted successfully
Complete
The session timeline and selected review events were preserved with the completed test.
Organize face events without treating every signal as misconduct.
AI-assisted analysis can help prioritize events using configured rules, duration, frequency, person count, camera continuity, and repeated patterns. Final decisions should include complete evidence, candidate context, policy, and human judgement.
Candidate continuity
96%
Face-presence continuity
91%
Illustrative review confidence
86%
Face monitoring policy controls
Configure a fair and proportionate face-detection process
Define camera requirements, candidate instructions, lighting and framing guidance, face-absence thresholds, multiple-person rules, technical recovery, accommodations, reviewer responsibilities, privacy notices, retention, and escalation procedures.
Preparation
Camera, lighting, and candidate-position guidance
Explain camera placement, face visibility, lighting, background, seating position, device stability, network readiness, and troubleshooting before the test.
Absence rules
Face-absence duration and recurrence rules
Configure whether brief visibility loss, repeated absence, extended absence, obstruction, or camera movement should create a review event.
Person count
Multiple-person and additional-face settings
Define how additional-person signals are recorded, prioritized, reviewed, explained, and connected with environmental context.
Recovery
Camera interruption and technical-support process
Provide clear recovery steps for denied permissions, connection loss, stream interruption, unsupported devices, browser issues, and assessment resumption.
Inclusion
Accessibility and approved accommodations
Support approved camera exceptions, movement needs, breaks, assistive technology, additional time, environmental adjustments, and alternative review arrangements.
Governance
Privacy, retention, review, and decision controls
Define consent notices, data use, retention period, reviewer permissions, decision notes, candidate explanation, escalation, audit history, and reconsideration procedures.
Face detection report
Illustrative face-continuity assessment summary
94
continuity index
Session completed with limited face-review events
Candidate identity remained consistent, face visibility was maintained for most of the assessment, and selected absence, additional-person, and camera events were preserved for contextual review.
Candidate identity continuity
97
Face-presence continuity
93
Camera and visibility continuity
89
Session evidence quality
85
Reviewer summary
Illustrative session recommendation
FACE
Remote assessment session
AI-assisted face-event review
Review complete
Candidate verification
Configured access, identity, camera, and system checks were
completed successfully.
Verified
Face-presence continuity
Candidate face visibility remained consistent during most of
the assessment.
94%
Review events
Two brief absences, one additional-person signal, and one
camera interruption were recorded.
4
Recommended action
Retain the result with the completed evidence record,
candidate context, and reviewer decision notes.
Proceed
Face-detection signals should support review, not replace
judgement
Final decisions should consider the complete evidence record, technical conditions, candidate explanation, approved accommodations, assessment policy, privacy obligations, and authorized human review.
Face detection proctoring use cases
Support remote assessments across recruitment, education, and certification
Use CloudTest Face Detection Proctoring for recruitment assessments, coding tests, campus hiring, academic examinations, certification programmes, scholarship tests, employee evaluations, entrance tests, and distributed candidate screening.
Recruitment assessments
Support aptitude, technical, behavioural, role-based, and job-readiness tests with configurable candidate-presence monitoring.
Coding and technical tests
Connect face-presence evidence with browser, editor, compilation, execution, and coding-session activity.
Campus and graduate hiring
Apply consistent candidate verification, face monitoring, event review, and reporting across high-volume remote assessments.
Academic examinations
Support remote quizzes, practical tests, term examinations, entrance assessments, and scholarship programmes.
Certification programmes
Protect professional, compliance, technical, product, partner, and skill-validation assessments conducted remotely.
Internal employee assessments
Evaluate employees for promotion, internal mobility, technical progression, training completion, and organisational certification.
Face detection proctoring benefits
Build transparent face-monitoring evidence for remote assessments
Create a structured process for candidate entry, camera readiness, face-presence tracking, event timestamps, technical interruptions, reviewer notes, candidate explanations, decision history, and audit records.
Standardize candidate-presence monitoring
Apply consistent identity, camera, face-presence, multiple-person, technical recovery, review, and reporting rules.
Consistent monitoringCreate chronological face evidence
Connect face visibility, absence, multiple-person, and camera events with timestamps, duration, recurrence, and session context.
Time-linked evidenceReduce manual monitoring effort
Use AI-assisted event organization to help reviewers focus on selected moments instead of manually observing every complete session.
Efficient reviewSupport responsible human decisions
Preserve evidence, technical context, candidate explanations, accommodations, reviewer comments, policy references, and decision history.
Human oversightFrequently asked questions
Face Detection Proctoring FAQs
Learn how CloudTest supports face-presence monitoring, multiple-person detection, temporary absence tracking, camera interruption logs, session evidence, privacy, accommodations, human review, and remote assessment integrity.
What is Face Detection Proctoring?
Face Detection Proctoring uses configured camera and face-presence signals to support remote assessment monitoring. These signals may include face visibility, temporary absence, additional-person events, camera interruptions, timestamps, duration, and session continuity.
Can the system detect when a candidate leaves the camera frame?
Configured face-absence events can be recorded when the candidate is no longer visible, becomes obstructed, moves outside the camera frame, or the camera stream is interrupted.
Can multiple people or faces be detected?
Multiple-person or additional-face signals can be recorded where technically supported. These signals should be reviewed with duration, frame position, environment, technical conditions, and candidate explanation.
Does every face-absence event indicate misconduct?
No. Face visibility may be affected by movement, lighting, connectivity, camera positioning, technical issues, approved breaks, accessibility needs, or environmental interruptions. Each event should be reviewed in context.
Can different assessments use different face-detection rules?
Recruitment tests, coding assessments, certification programmes, academic examinations, scholarship tests, and internal evaluations can use different camera requirements, absence thresholds, person-count settings, and review procedures.
What information can be included in a face-detection report?
Reports can include candidate verification, face-presence continuity, temporary absence events, multiple-person signals, camera interruptions, timestamps, duration, recurrence, reviewer notes, candidate explanations, and final review status.
How should privacy and consent be handled?
Candidates should receive clear information about camera use, monitored signals, data purpose, retention, permitted behaviour, technical support, reviewer access, available accommodations, and applicable privacy procedures before the assessment begins.
Should face-detection AI make the final integrity decision?
Face-detection signals should support an authorized review process. Final decisions should include complete session evidence, technical conditions, candidate context, approved accommodations, organizational policy, and appropriate human judgement.
Ready to protect remote assessments?
Build transparent, reviewable, and candidate-aware face-detection workflows with CloudTest.
Configure candidate verification, camera readiness, face-presence monitoring, multiple-person signals, temporary absence tracking, interruption logs, review thresholds, privacy notices, accommodations, and detailed proctoring reports.