Generative face alignment through 2.5D active appearance models
نویسندگان
چکیده
This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas-Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the selfocclusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D+3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation. Preprint submitted to CVIU Computer Vision and Image Understanding
منابع مشابه
Face Alignment Through 2.5D Active Appearance Models
This work addresses the fitting of 3D deformable face models from a single view through 2.5D Active Appearance Models (AAM). The main contribution of this paper is the use of 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by full perspective projections of the PDM. The advantage is that, assuming a calibrated camera, ...
متن کاملFace Alignment by 2.5D Active Appearance Model Optimized by Simplex
In this paper we propose an efficient algorithm to align the face in real time, based on Active Appearance Model (AAM) in 2.5D. The main objective is to make a robust, rapid and memory efficient application suitable for embedded systems, so they could align the pose rapidly by using less memory. Classical AAM is a high memory consumer algorithm, consequently transfer of this stored memory in an...
متن کامل2.5D Visual Speech Synthesis Using Appearance Models
Two dimensional (2D) shape and appearance models are applied to the problem of creating a near-videorealistic talking head. A speech corpus of a talker uttering a set of phonetically balanced training sentences is analysed using a generative model of the human face. Segments of original parameter trajectories corresponding to the synthesis unit (e.g. triphone), are extracted from a codebook, th...
متن کاملMulti-band modelling of appearance
Earlier work has demonstrated generative models capable of synthesising near photo-realistic grey-scale images of objects. These models have been augmented with colour information, and recently with edge information. This paper extends the Active Appearance Model framework by modelling the appearance of both derived feature bands and an intensity band. As a special case of featureband augmented...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Computer Vision and Image Understanding
دوره 117 شماره
صفحات -
تاریخ انتشار 2013