Facial recognition technology is comprised of three basic technologies, namely, face detection technology, feature point extraction technology, and face matching technology.Face detection technology detects the ‚Äúposition of the face‚Ä? within an image, while feature extraction technology finds the ‚Äúposition of facial feature points,‚Ä? such as the pupil, subnasal point, mouth corners, etc.
Face matching technology determines the ‚Äúidentity‚Ä? of the detected face. The major three features of this system are:
Generalized Learning Vector Quantization (GLVQ) is used over here.Rectangular areas that match the face are extracted by sequentially searching face areas starting from the edge of the image. The Generalized Learning Vector Quantization (GLVQ) algorithm, which is based on the Minimum Classification Error criterion, is used to recognize whether areas are face areas or not, enabling fast and accurate face detection functions.
Multiple feature point detection method is used here.This method is used to find the position of feature points, such as the pupil, subnasal point, and corners of the mouth. Brightness patterns around the feature points are used to find the most optimum position, while the facial shape model is used to constrain the alignment of feature points, enabling precise estimation of their positions.
Multidimensional feature recognition method is used here.The most appropriate feature to recognize an individual is chosen after extracting features from the face, such as facial contours and tilt. This enables robust personal identification that is unaffected by changes due to aging and other factors.