Coordinate-Based Geometric Features and Nearest Neighbor Performance in 2D Facial Classification

Geometric feature is one of many kinds of feature that is used in facial recognition system. This feature represents the relative position between facial objects. In recent days, those relative positions are represented by facial landmarks. Many extracted features from facial landmark had been used in facial recognition, except the coordinate-based feature. This paper transforms raw facial landmarks coordinate into coordinate-based geometric features. The raw two dimensions facial landmarks coming from 68-points facial landmark dataset. The feature extraction process utilizes two points of outer eye (OE), right OE is placed at (0,0) and the left OE is placed at (100, 0) using translation, rotation, and scaling matrices. There are 208 class in classification process. This process involves 208 subjects, where each subject has one file in probe image and nine files in gallery images. The k-NN (nearest neighbor) with k=1 is chosen as classification method. On test session, the use of 11 to 17 features is resulting high recognition accuracy (more than 90%) with 94.23% at the peak. Compared to other studies which use triangular-based feature, angular-based feature, and distance-based feature, coordinate-based feature gives better accuracy.