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The emotion recognition methodology integrates precise face detection and landmark extraction using the SPIGA detector. Landmarks are then aligned by shifting centered on the nasal bridge, enhancing uniformity. Differences between neutral and expressive poses generate 98 feature vectors per emotion. These vectors train different classifiers.

active 2024-02-16 → 2024-08-25 (UTC)

Complete coverage28,473 / 28,473 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-11-13 (UTC)
Events
7
Pushes
4
Pull requests
0
Issues
0
Stars
1
Forks
0

Activity over time

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Line chart, 192 days from 2024-02-16 to 2024-08-25. Pushes: 4 total, peak 3 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.

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Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

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trutadan4400

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Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.