Analysis and Synthesis of Human Faces with Pose Variations by a Parametric Piecewise Linear Subspace Method

نویسندگان

  • Kazunori Okada
  • Christoph von der Malsburg
چکیده

A framework for learning an accurate and general parametric facial model from 2D images is proposed and its application for analyzing and synthesizing facial images with pose variation is demonstrated. Our parametric piecewise linear subspace method covers a wide range of pose variation in a continuous manner through a weighted linear combination of local linear models distributed in a pose parameter space. The linear design helps to avoid typical non-linear pitfalls such as over tting and time-consuming learning. Experimental results show sub-degree and sub-pixel accuracy within 55 degree full 3D rotation and good generalization capability over unknown head poses when learned and tested for speci c persons.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Pose-Invariant Face Recognition: Representing Known Persons by View-based Statistical Models

We present a framework for pose-invariant face recognition using parametric linear subspace models as stored representations of known individuals. Each model can be t to an input, resulting in faces of known people whose head pose is aligned to the input face. The model's continuous nature enables the pose alignment to be very accurate, improving recognition performance, while its generalizatio...

متن کامل

Learning a Warped Subspace Model of Faces with Images of Unknown Pose and Illumination

In this paper we tackle the problem of learning the appearances of a person’s face from images with both unknown pose and illumination. The unknown, simultaneous change in pose and illumination makes it difficult to learn 3D face models from data without manual labeling and tracking of features. In comparison, image-based models do not require geometric knowledge of faces but only the statistic...

متن کامل

Pose - Invariant Face Recognition with Parametric Linear

We present a framework for pose-invariant face recognition using parametric linear subspace models as stored representations of known individuals. Each model can be t to an input, resulting in faces of known people whose head pose is aligned to the input face. The model's continuous nature enables the pose alignment to be very accurate, improving recognition performance, while its generalizatio...

متن کامل

Analysis and Synthesis of Pose Variations of Human Faces by a Linear PCMAP Model and its Application for Pose-Invariant Face Recognition System

A method of manifold representation for human faces with pose variations is proposed. Our model consists of mappings between 3D head angles and facial images separately represented in shape and texture, via sub-space models spanned by principal components (PCs). Explicit mappings to and from 3D head angles are used as processes of pose estimation and transformation, respectively. Generalization...

متن کامل

Face Recognition Under Varying Viewing Conditions with Subspace Distance

We present a feature-invariant classification model that recognizes images under various analytic and nonanalytic transformations in the category of face recognition where human faces to be recognized are seen under varying lighting conditions and viewpoints. Our method exploits the idea of tangent approximation to differentiable manifolds, which motivates the use of subspace distance to build ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001