Face Gesture Stylizer: Real-Time Facial and Hair Restyling Using Hand Gesture Recognition and Computer Vision

Face Gesture Stylizer is a webcam-based computer-vision application that applies artistic transformations to a user’s face and hair while retaining the natural appearance of the hands, neck, clothing and background. The application uses hand gestures to control the live effect: a right or left swipe changes the selected style, an upward or downward swipe adjusts blend intensity, and a sustained thumb–index pinch requests a photograph capture. The browser workflow uses MediaPipe Tasks Vision for hand landmark detection and selfie multiclass segmentation. A gesture engine interprets landmark movement over time, while a compositor applies the selected effect through an estimated head mask and preserves the hand region as a foreground object. The interface provides 24 built-in artistic styles and an optional FLUX.2 [klein] generative-editing path when a compatible backend is configured. The supplied screenshots demonstrate the live interface and three selected styles. Instantaneous frame-rate readings of 15–19 FPS are visible in the captures; these are observations rather than controlled benchmark results.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-06
DOI
https://doi.org/10.5281/zenodo.24014027
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Face Gesture Stylizer: Real-Time Facial and Hair Restyling Using Hand Gesture Recognition and Computer Vision

Srijan Mohan, Puja Gupta, Rishab Patidar
Zenodo (CERN European Organization for Nuclear Research)
Hand Gesture Recognition Systems
article

Face Gesture Stylizer: Real-Time Facial and Hair Restyling Using Hand Gesture Recognition and Computer Vision

Srijan Mohan, Puja Gupta, Rishab Patidar
article en

Abstract

Face Gesture Stylizer is a webcam-based computer-vision application that applies artistic transformations to a user’s face and hair while retaining the natural appearance of the hands, neck, clothing and background. The application uses hand gestures to control the live effect: a right or left swipe changes the selected style, an upward or downward swipe adjusts blend intensity, and a sustained thumb–index pinch requests a photograph capture. The browser workflow uses MediaPipe Tasks Vision for hand landmark detection and selfie multiclass segmentation. A gesture engine interprets landmark movement over time, while a compositor applies the selected effect through an estimated head mask and preserves the hand region as a foreground object. The interface provides 24 built-in artistic styles and an optional FLUX.2 [klein] generative-editing path when a compatible backend is configured. The supplied screenshots demonstrate the live interface and three selected styles. Instantaneous frame-rate readings of 15–19 FPS are visible in the captures; these are observations rather than controlled benchmark results.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Hand Gesture Recognition Systems
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