feat: Implement empirical confidence calibration for face matching

This commit introduces a new confidence calibration system that converts DeepFace distance values into actual match probabilities, addressing previous misleading confidence percentages. Key changes include the addition of calibration methods in `FaceProcessor`, updates to the `IdentifyPanel` and `AutoMatchPanel` to utilize calibrated confidence, and new configuration settings in `config.py`. The README has been updated to document these enhancements, ensuring users see more realistic match probabilities throughout the application.

README.md

@@ -277,27 +277,6 @@ PunimTag Development Team
 - **Similarity**: Cosine similarity (industry standard for deep learning embeddings)
 - **Accuracy**: Significantly improved over previous face_recognition library
 
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-
-## 🔧 Recent Updates
-
-### Face Orientation Fix (Latest)
-**Fixed face orientation issues in the identify functionality**
-
-- ✅ **Resolved rotated face display**: Faces now show in correct orientation instead of being rotated
-- ✅ **Fixed false positive detection**: Eliminated detection of clothes/objects as faces for rotated images
-- ✅ **Improved face extraction**: Fixed blank face crops by properly handling EXIF orientation data
-- ✅ **Comprehensive EXIF support**: Full support for all 8 EXIF orientation values (1-8)
-- ✅ **Consistent processing**: Face detection and extraction now use consistent orientation handling
-
-**Technical Details:**
-- Applied EXIF orientation correction before face detection to prevent false positives
-- Implemented proper coordinate handling for all orientation types
-- Enhanced face extraction logic to work with corrected images
-- Maintained backward compatibility with existing face data
-
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-
 ### Migration Documentation
 - [Phase 1: Database Schema](PHASE1_COMPLETE.md) - Database updates with DeepFace columns
 - [Phase 2: Configuration](PHASE2_COMPLETE.md) - Configuration settings for DeepFace