C. von der Malsburg

A feature based approach to face recognition

A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated.<<ETX>>

A system for person-independent hand posture recognition against complex backgrounds

A computer vision system for person-independent recognition of hand postures against complex backgrounds is presented. The system is based on the elastic graph matching, which was extended to allow for combinations of different feature types at the graph nodes.

Strategies and Benefits of Fusion of 2D and 3D Face Recognition

The extension of 2D image-based face recognition methods with respect to 3D shape information and the fusion of both modalities is one of the main topics in the recent development of facial recognition. In this paper we discuss different strategies and their expected benefit for the fusion of 2D and 3D face recognition. The face recognition grand challenge (FRGC) provides for the first time ever a public benchmark dataset of a suitable size to evaluate the accuracy of both 2D and 3D face recognition. We use this benchmark to evaluate hierarchical graph matching (HGM), an universal approach to 2D and 3D face recognition, and demonstrate the benefit of different fusion strategies. The results show that HGM yields the best results presented at the recent FRGC workshop, that 2D face recognition is significantly more accurate than 3D face recognition and that the fusion of both modalities leads to a further improvement of the 2D results.

Face recognition by elastic bunch graph matching

We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from Lades et al. (1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs.

Online facial expression recognition based on personalized galleries

An online facial expression recognition system based on personalized galleries is presented. This system is built on the framework of the PersonSpotter system, which is able to track and detect the face of a person in a live video sequence. By utilizing the recognition method of Elastic Graph Matching, the most similar person whose images are stored in the gallery can be found, then the personalized gallery of this person is used to recognize the expression on the probe face. A personalized gallery consists of images of the same person showing different facial expressions. Node weighting and weighted voting in addition to Elastic Graph Matching are applied to identify the expression. The performance achieved by this system shows its great potential.

Self-organized integration of adaptive visual cues for face tracking

A mechanism for the self-organized integration of different adaptive cues is proposed. In democratic integration the cues agree on a result and each cue adapts towards the result agreed upon. A technical formulation of this scheme is employed in a face tracking system. The self-organized adaptivity lends itself to suppression and recalibration of discordant cues. Experiments show that the system is robust to sudden changes in the environment as long as the changes disrupt only a minority of cues at the same time, although all cues may be affected in the long run.

A gesture interface for human-robot-interaction

The authors present a person-independent gesture interface implemented on a real robot which allows the user to give simple commands, e.g., how to grasp an object and where to put it. The gesture analysis relies on real-time tracking of the users hand and a refined analysis of the hands shape in the presence of varying complex backgrounds.

Pose-invariant face recognition with parametric linear subspaces

We present a framework for pose-invariant face recognition using parametric linear subspace models as stored representations of known individuals. Each model can be fit to an input, resulting in faces of known people whose head pose is aligned to the input face. The models continuous nature enables the pose alignment to be very accurate, improving recognition performance, while its generalization to unknown poses enables the models to be compact. Recognition systems with two types of parametric linear model are compared using a database of 20 persons. The results showed our systems robust recognition of faces with /spl plusmn/50 degree range of full 3D head rotation, while compressing the data by a factor of 20 and more.

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 (PC). Explicit mappings to and from 3D head angles are used as processes of pose estimation and transformation, respectively. Generalization capability to unknown head poses enables our model to continuously cover pose parameter space, providing high approximation accuracy. The feasibility of this model is evaluated in a number of experiments. We also propose a novel pose-invariant face recognition system using our model as the entry format for a gallery of known persons. Experimental results with 3D facial models recorded by a Cyberware scanner show that our model provides a superior recognition performance against pose variations, and that the texture synthesis process is carried out correctly.

Analysis and synthesis of human faces with pose variations by a parametric piecewise linear subspace method

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 nonlinear pitfalls such as overfitting and time-consuming learning. Experimental results show sub-degree and sub-pixel accuracy within /spl plusmn/55 degree full 3D rotation and good generalization capability over unknown head poses when learned and tested for specific persons.