Face and Person Detection in Images

Face detection, person detection and face recognition are related but distinct tasks. Detection locates a face or person, while recognition evaluates identity similarity between an observed face and stored representations.

Detection and alignment

Haar cascades and HOG-based methods historically provided low-cost solutions. Modern learned detectors are more robust to changes in pose, scale and occlusion.

Facial landmarks can align the eyes, nose and mouth before recognition, reducing variation caused by crop position and head pose.

Embedding-based recognition

A recognition network maps the aligned face to an embedding. Samples of the same identity are encouraged to lie close together, while different identities are separated. Open-set systems must also support a no-match decision for people not enrolled in the database.

Face detection quality constrains every later stage. Poor cropping or alignment can shift the resulting embedding.

Evaluation

False-match and false-non-match rates should be interpreted together with the decision threshold. Camera type, pose, illumination, age and data distribution can affect these rates. Access control and data minimisation are also architectural requirements for identity-processing systems.