Face Protrusion Detection Using Contrast Changes Under Varying Lighting

The system captures multiple images of the same face under different lighting conditions (lighted and non-lighted states) and dynamically analyzes the contrast changes between these images. This dynamic approach allows a simple 2D camera to detect protrusions by observing how light interacts with facial features, thereby maintaining low device complexity while improving authentication reliability.

The system changes the lighting parameter (from lighted to non-lighted state) and measures the resulting contrast changes in facial regions. By analyzing how contrast values change when lighting conditions change, the system can determine protruding degrees of facial features, enabling reliable distinction between 2D images and real persons using only a 2D camera.

Instead of using complex 3D imaging hardware, the system creates multiple 2D copies of the same scene under different lighting conditions and analyzes the differences between these copies. By comparing contrast values in lighted and non-lighted images, the system can infer three-dimensional protrusion information from two-dimensional data, achieving accurate protrusion detection without 3D camera complexity.

The system replaces the mechanical/optical complexity of 3D cameras and stereo reconstruction algorithms with a simpler approach using a single 2D camera. By substituting the imaging mechanism with a computational analysis of contrast changes under varying lighting, the system achieves the same protrusion detection accuracy with significantly reduced device complexity.