Lightweight Facial Landmark Detection for Real-Time Face Shape Classification

A lightweight, landmark-driven framework for real-time face shape classification is proposed, eliminating the need for deep neural architectures. Fourteen critical facial points are extracted using MediaPipe Face Mesh, from which five scale-invariant geometric features—jawline width, cheekbone width, face length, forehead width, and mouth width—are computed to form a concise feature vector. A shallow decision tree (maximum depth = 6) yields an overall accuracy of 91.7 % across six canonical face shape categories on a 14 378-image dataset, with per-frame processing time under 2 ms and sustained throughput of 25–28 FPS. Because of its minimal memory and processing needs, the technique can be used in resource-constrained settings including healthcare applications, virtual try-on services, and mobile augmented reality.