Multi-view, High-resolution Face Image Analysis

With advances in digital photography, people can obtain large-scale and high-quality pictures more easily. How to understand this large-scale and high-quality information and how to make use of this information to recover distortions in other images are two fundamental and challenging problems in computer vision and image processing. In this thesis, we solve these problems for face images so that facial-image analysis and recognition can be performed more efficiently and accurately. In this thesis, we will mainly focus on the following three areas: face matching, and face verification/recognition, and color correction. Establishing correct correspondences between two faces with different viewpoints has played an important role in 3D face reconstruction and other computer-vision applications. Usually, face images are considered to lack sufficient distinctive features to track their geometry. Hence, existing methods have to rely on other man-made features such as structured lighting, special makeup, and markers. These active methods need specific devices to capture an object’s structure. We investigate pore-scale facial features, which have many characteristics that make them suitable for matching face images under different variations. To alleviate the effect of changing skin conditions, a new framework is proposed as a trade-off between robustness and completeness. Based on this framework, a method adapted from scale-invariant feature transform (SIFT), namely pore-SIFT (PSIFT), is proposed, which is an automatic, passive approach for extracting distinctive pore-scale facial features for the reliable matching of uncalibrated face images. To improve the performance of face verification/recognition using high-resolution (HR) information and the robustness to misalignment, we propose an alignment-free and pose-invariant face-verification method using the HR information based on porescale facial features. Most current face-verification/recognition methods represent face images mainly based on the holistic or local facial features. This makes these methods rely heavily on face alignment, so their performances degrade severely under variations in expression and/or pose, especially with only one gallery per subject. In this thesis, we have proposed a new keypoint descriptor called pore-PCASIFT, which is adapted from PCA-SIFT and is used for the extraction of compact, distinctive porescale facial features. Furthermore, a more effective feature matching scheme is pro-