Deep learning models (FaceNet, ArcFace) for facial embeddings. Face detection, alignment, feature extraction. Robust to lighting, angle, quality variations.
GAN-based models to estimate appearance across decades. Training on longitudinal datasets. Handles aging from childhood to elderly. Accuracy improves with multiple reference photos.
AI enhancement of old, damaged photos. Super-resolution to improve quality. Colorization of black & white images. Noise reduction, scratch/tear removal.
Matching photos taken 50+ years apart. Invariant facial features: bone structure, eye placement, ear shape. Confidence scores account for time gap and photo quality.
Automatic generation of family trees from photo collections. Facial similarity analysis to identify relatives. Timeline visualization of family photos.