Facial Recognition

Facial recognition systems detect faces in camera images using trained detectors, then extract distinctive facial features. Deep neural networks encode faces as high-dimensional feature vectors (embeddings) where similar faces have similar vectors. During enrollment, the system stores reference embeddings for known individuals. During recognition, the system compares new face embeddings against the database using similarity metrics. Matches exceeding a threshold are accepted as identified individuals. Advanced systems handle variations in lighting, angle, expression, and aging. Liveness detection prevents spoofing with photos. Privacy-preserving systems can perform recognition without storing raw images, using only encrypted embeddings.

Facial recognition enables humanoid receptionists to greet returning visitors by name. Healthcare robots identify patients and retrieve medical histories. Security robots verify authorized personnel and alert on unrecognized individuals. Smart home robots personalize services - music preferences, lighting, daily routines - based on who is present. Educational robots adapt teaching content to identified students. Retail robots recognize VIP customers for personalized service. Social robots build long-term relationships by remembering past interactions. Accessibility features activate when recognizing users with specific needs.