Face Swap & Deepfake

Where It's Applied

In my practice, I developed a real-time face swap system for entertainment and interactive purposes. The system works both locally on the user's device and on a server, processing video stream from the camera and replacing the user's face with another (celebrity face, fictional character, or another person). At company events, I used this as an interactive entertainment installation where participants could instantly see themselves with famous personalities' faces, creating a memorable and viral-worthy experience.

Who Will Benefit

I recommend this solution to companies hosting events, conferences, and corporate celebrations wanting to create interactive entertainment for participants. Media companies and bloggers for creating content and entertainment videos. Companies working with video conferences wanting to add a fun element (e.g., meeting with altered faces as a joke). Bars, clubs, and other entertainment venues for interactive photo booths. Marketing agencies for creating viral content. Educational institutions for demonstrating AI technology capabilities.

Technologies

InsightFace as the Foundation for Face Detection and Transformation

I use InsightFace as the main component for detecting faces in video and extracting their characteristics. The system detects the user's face in each video frame, aligns it (face alignment) to standardize position and orientation, and extracts detailed facial landmarks (eye, nose, mouth positions, etc.). Based on these landmarks, the system can transform the face and prepare it for replacement.

Face Swap Models

For the actual face replacement, I use specialized face swap models: DeepFaceLive (based on autoencoder architecture) or SimSwap (more advanced architecture with better target face identity preservation). These models are trained on huge facial datasets and can realistically replace faces while preserving facial expressions, gaze direction, and original video movements.

Process: system extracts user's face from video → finds target face (celebrity) → uses face swap model to replace user's face with target while preserving mimicry and movements → synthesizes result and overlays on video.

Real-Time Video Stream Processing

For live operation, I process the camera video stream asynchronously: each frame is captured via OpenCV, processed (face detection, swap), and sent back to the screen. On GPU (NVIDIA CUDA), the system processes 20-30 fps (frames per second) in Full HD, creating a live broadcast impression without noticeable delays.

In practice: users see themselves on screen with a different face with only 100-200ms latency, sufficient for an interactive experience.

Local Deployment Without Cloud

All computations run locally on a laptop or event server without sending video to the cloud. This is critical for: confidentiality (video doesn't reach external servers), speed (no network interaction delays), reliability (system works without internet). On a laptop with RTX 3070, I can process multiple streams simultaneously, enabling concurrent work with several event participants.

Target Face Gallery Management

The system works with a gallery of target faces (celebrities, characters, etc.). For each target face, the system pre-extracts and stores its characteristics (embeddings) for faster processing. Event participants can choose which face they prefer ("Tom Cruise's face", "Marilyn Monroe's face", "Elon Musk's face") and see themselves in real time with that face.

Facial Expression and Mimicry Processing

The system preserves the original face's mimicry: if the user smiles, the target face smiles too; if they blink, the target face blinks as well. This makes the result much more realistic than simple face replacement. In practice: users can smile, blink, move their head, and all these movements are preserved in the target face, creating the impression that it's actually the celebrity's face with their mimicry.

Smoothing and Post-Processing

After face swap, the video undergoes post-processing: edge smoothing (blending) between the replaced face and original body, lighting correction to match the background, artifact removal. I use OpenCV and specialized filters to create smooth transitions between face and body for a natural-looking result.

Video Capture and Export

At events, I added video recording functionality: users can record 10-15 seconds of video with their swapped face, and the system saves the video or image. Participants can then download videos, share on social media, creating viral content for the company. I use FFmpeg for video encoding and compression, enabling quick creation of ready-to-download files.

Video Conference Integration

For video conferences (Zoom, Teams, Google Meet), I created a virtual camera plugin that works as an intermediate layer between the real camera and the video conference application. Users launch my plugin, select a target face, and conference participants see them with a swapped face. In practice: this creates a fun atmosphere in meetings and lets people meet anonymously or as a joke.

Web Interface for Management

I created a web application (Vue.js + FastAPI) for system management: uploading target faces, selecting processing parameters (swap intensity, smoothing level), managing video recording. The interface is intuitive and requires no technical knowledge — event participants can immediately start using the system.

GPU CUDA Acceleration

All computations use NVIDIA GPU with CUDA: face detection, landmark extraction, face swap, post-processing. On CPU the system would be too slow (several seconds per frame). On GPU (CUDA), processing one Full HD frame takes 30-50ms, enabling 20-30 fps real-time performance.

At an event with 4000 participants, I used an RTX 5090 machine that could simultaneously serve 5-6 participants with live face swap without queues.

Scaling for Mass Events

For large events, the system must serve multiple users. I organized this through: multiple independent application instances on different machines, load balancing (distributing participants across available machines), result caching (if multiple users want the same target face, results can be reused).

Demo Photo Booth at Event

At a corporate event, I deployed an interactive installation: screen with live video (participant's face with face swap), with life-sized cardboard cutouts of celebrities nearby, where participants could stand next to their "celebrity version" and take photos. Generated photos and videos were automatically uploaded to the company's social media, creating organic viral content. Participants actively shared results, giving the company massive social media reach.

Ethical and Legal Aspects

The system was developed explicitly for entertainment. I always ensure: the system is used only at the actual event in controlled conditions, participants know it's entertainment (not deception), videos/images are saved only with participant consent, the system isn't used to create illegal content or deceive third parties.

Technically, the system can create deepfakes, which may be illegal in some jurisdictions. I always consult with the company's legal department before deploying at an event.

Important Organizational Considerations

First — participant consent and information. Before using face swap, the system must obtain user consent. I use a prominent banner "THIS IS ENTERTAINMENT, NOT REAL", and participants must explicitly agree before video is processed or saved.

Second — target face selection. I always choose public celebrity faces with explicit permission for image use (or already public figures). I never use private individuals' faces without consent.

Third — GPU quality and throughput. At events with many participants, powerful GPU is needed (RTX 3090+ or RTX 5090). I recommend pre-load testing to ensure the system handles expected participant volume.

Fourth — backups and fallback faces. If a target face is unavailable or the model performs poorly, the system needs fallback options. I always prepare several backup faces so participants can choose alternatives.

Fifth — photo and video handling after the event. After receiving videos/photos, participants typically want to download them. I created a simple portal where participants can download their content, share on social media, or send to friends.

Sixth — on-site monitoring and technical support. On-site technical support should be available to help participants with issues (camera problems, system freezes, low video quality). I always prepare documentation for technical staff with troubleshooting instructions.