We propose a facial alignment algorithm that is able to jointly deal with the presence of facial pose variation, partial occlusion of the face, and varying illumination and expressions. Our approach proceeds from sparse to dense landmarking steps using a set of specific models trained to best account for the shape and texture variation manifested by facial landmarks and facial shapes across pose and various expressions. We also propose the use of a novel l1-regularized least squares approach that we incorporate into our shape model, which is an improvement over the shape model used by several prior Active Shape Model (ASM) based facial landmark localization algorithms. Our approach is compared against several state-of-the-art methods on many challenging test datasets and exhibits a higher fitting accuracy on all of them.

In this paper, we propose a novel system for beard and mustache detection and segmentation in challenging facial images. Our system first eliminates illumination artifacts using the self-quotient algorithm. A sparse classifier is then used on these self-quotient images to classify a region as either containing skin or facial hair. We conduct experiments on the MBGC and color FERET databases to demonstrate the effectiveness of our proposed system.

Segmentation of facial features is a key pre-processing step in enabling facial recognition, building of 3D facial models, expression analysis, and pose estimation. Recently, graph cuts based algorithms have been adapted to carry out this task but many of these methods require manual initialization of points in the foreground and background. In this paper, we propose a novel and fully automatic approach, named Face-Cut, to perform accurate facial feature segmentation. FaceCut combines the positive features of the Modified Active Shape Model (MASM) and GrowCut algorithms to ensure highly accurate and completely automatic segmentation of facial features. We demonstrate the effectiveness of FaceCut on images from two challenging databases.

Active Shape Models (ASMs) have recently gained popularity for performing automatic facial landmark fitting. Their demonstrated ability to generalize and fit unseen faces make them ideal candidates for this task unlike the traditional Active Appearance Model (AAM)-based approaches, which have difficulty in accurately landmarking unseen images. Given a test image, a face detector is used to determine the locations, orientations and sizes of faces in the image. Facial landmarking algorithms, such as ASMs, are initialized based on these parameters. In this paper, we conduct a series of experiments to exhaustively evaluate the tolerance of three popular ASMs to initialization perturbations (translation, rotation, and scaling in size) of the face detected, a topic that has not been analyzed in depth to date. Our results are consistent across different databases, provide an understanding of the role initialization plays in the landmark fitting process and serve as a performance gauge that could be considered when comparing facial landmarking algorithms.

In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holistic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Contourlet Transform (NSCT). We demonstrate the efficiency of our method by applying it to the problem of facial age estimation. Compared to previously published age estimation techniques, our approach yields more accurate results when tested on various face aging databases.

Traditional approaches to face recognition have utilized aligned facial images containing both shape and texture information. This paper analyzes the contributions of the individual facial shape and texture components to face recognition. These two components are evaluated independently and we investigate methods to combine the information gained from each of them to enhance face recognition performance. The contributions of this paper are the following: (1) to the best of our knowledge, it is the first large-scale study of how face recognition is influenced by shape and texture as all of our results are benchmarked against traditional approaches on the challenging NIST FRGC ver2.0 experiment 4 dataset, (2) we empirically show that shape information is reasonably discriminative, (3) we demonstrate significant improvement in performance by registering texture with dense shape information, and finally (4) show that fusing shape and texture information consistently boosts recognition results across different subspace-based algorithms.