Face Detection and Modeling for Recognition By Rein-Lien Hsu Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated re...

3D face has been introduced in the literature to deal with the unsolved issues of 2D face recognition, namely lighting and pose variations. In this paper, we study and compare the distinctiveness of features extracted from both the registered 2D face images and 3D face models. Sparse Representation Classifier (SRC) is exploited to calculate all similarity measures which are compared with the on...

In this paper, we propose a novel face recognition approach based on 2.5D/3D shape matching. While most of existing methods use facial intensity image, we aim to develop a method using three-dimensional information of the human face. This is the main innovation of our technology. In our approach, the 3D dimensional information is introduced in order to overcome classical face recognition proble...

2 ABSTRACT We explore the field of automated face recognition. Beginning with a survey of existing methods applied to two-dimensional (2D) and three-dimensional (3D) face data, we focus on subspace techniques, investigating the use of image pre-processing applied as a preliminary step in order to reduce error rates. We implement the eigenface and Fisherface methods of face recognition, computin...

The performances of most existing face recognition systems suffer from facial expressions. Unfortunately, there is not yet a satisfactory solution. Therefore, the main focus of this thesis is on expression-invariant face recognition algorithms. In this thesis, we first propose a 2D face recognition algorithm by separately modeling geometry and texture information in a face image. The effect of ...

3D face recognition is an open field. In this paper we present a method for 3D facial recognition based on Principal Components Analysis. The method uses a relatively large number of facial measurements and ratios and yields reliable recognition. We also highlight our approach to sensor development for fast 3D model acquisition and automatic facial feature extraction.

Although deep learning techniques have achieved extraordinary accuracy in recognizing human faces, the pose variances of images captured real-world scenarios still hinder reliable model appliance. To mitigate this gap, we propose to recognize faces via generation frontal face with a 3D -Guided Deep P ose- I nvariant Face Recognition M odel (3D-PIM) consisted simulator and refiner module. The em...