Face verification with three-dimensional point cloud by using deep belief networks
Dong-Han Jhuang, Daw-Tung Lin, Chi-Hung Tsai · 2016
Developing reliable and robust face verification systems has been a tough challenge in computer vision, for several decades. The variation in illumination and head pose may seriously inhibit the accuracy of two-dimensional face recognition. With the invention of a depth map sensor, more three-dimensional volume data can be processed to mitigate the problem associated with face verification. This paper presents a three-dimensional face verification approach that includes three phases. First, point cloud library is applied to estimate features such as normal vectors and principal curvatures of every point on a human face point cloud acquired from three-dimensional depth sensor. Next, we adopt deep belief networks to train the identification model using extracted features. Finally, face verification is accomplished by using the pre-trained deep belief networks to justify if new incoming face point cloud feature is the one we specified. The experimental results demonstrate that the proposed system performs exceptionally well with about 96.43% verification accuracy.