A Study On Three-dimensional Optimized Face Recognition Model : Comparative Studies and Analysis of Model Architectures

Abstract

-In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

Analogical Face Generation based on Feature Points

“…The Point Signature method is used to extract depth information. The Point Signature is a method of expressing distance information as a one -dimensional spatial signal for angle, and Chua and Ho [6] [7] introduced the concept of Point Signature to face recognition [9].…”

“…The obtained 3D face shape obscures the face shape of the point cloud shape. It is also robust to illumination changes because it has depth information [6] [9].…”