Rainer Herpers

Edge and keypoint detection in facial regions

The authors introduce a method for the automatic detection of facial features and characteristic anatomical keypoints. In the application they are aiming at the anatomical landmarks are used to accurately measure facial features. Their approach is essentially bused on a selective search and sequential tracking of characteristic edge and line structures of the facial object to be searched. It integrates model knowledge to guarantee a consistent interpretation of the abundance of local features. The search and the tracking is controlled in each step by interpreting the already derived edge and line information in the context of the whole considered region. For their application, the edge and line detection has to be very precise and flexible. Therefore, they apply a powerful filtering scheme based on steerable filters.

An Attentive Processing Strategy for the Analysis of Facial Features

Facial landmarks such as eye corners, mouth corners or nose edges are important features for many applications in face recognition. The exact detection of these landmarks, however, is not an easy task because of the high individual variability of facial images and therefore, of the tremendous complexity of all the low-level features existing within the image. For instance, a precise and reliable detection of the eye corners has not been successfully solved until now. However, the knowledge of the exact position of these landmarks in the facial image is important for many matching and face processing tasks. For the classification and discrimination of dysmorphic facial signs a precise and reliable detection of a certain set of anatomical facial landmarks is particularly necessary. For this, an attentive processing strategy has been developed which puts the focus of the processing on only those salient image areas which are really needed to solve the several subtasks. The fundamental idea of the approach presented is to concentrate the artificial attention upon only a small fraction of the existing low-level features within a spatially well restricted image area.

The SVD approach for steerable filter design

The first processing step in computational early vision usually consists of convolutions with a number of kernels. These kernels often are derived from a mother kernel that is rotated, scaled, or deformed with respect to other degrees of freedom. This paper presents an efficient computational approach to calculate the responses of arbitrary mother kernels with arbitrary deformations. Analytical solutions to this problem in most cases are difficult or not possible. Therefore, a numerical approach that emphasizes an algebraical point of view is presented.

Dynamic Cell Structures for the Evaluation of Keypoints in Facial Images

In this contribution Dynamic Cell Structures (DCS network) are applied to classify local image structures at particular facial landmarks. The facial landmarks such as the corners of the eyes or intersections of the iris with the eyelid are computed in advance by a combined model and data driven sequential search strategy. To reduce the detection error after the processing of the sequential search strategy, the computed image positions are verified applying a DCS network. The DCS network is trained by supervised learning with feature vectors which encode spatially arranged edge and structural information at the keypoint position considered. The model driven localization as well as the data driven verification are based on steerable filters, which build a representation comparable with one provided by a receptive field in the human visual system. We apply a DCS based classifier because of its ability to grasp the topological structure of complex input spaces and because it has proved successful in a number of other classification tasks. In our experiments the average error resulting from false positive classifications is less than 1%.

Context based detection of keypoints and features in eye regions

Facial keypoints such as eye corners are important features for a number of different tasks in automatic face processing. The problem is that facial keypoints rather have an anatomical high-level definition than a low-level one. Therefore, they cannot be detected reliably by purely data-driven methods like corner detectors that are only based on the image data of the local neighborhood. In this contribution we introduce a method for the automatic detection of facial keypoints. The method integrates model knowledge to guarantee a consistent interpretation of the abundance of local features. The detection is based on a selective search and sequential tracking of edges controlled by model knowledge. For this, the edge detection has to be very flexible. Therefore, we apply a powerful filtering scheme based on steerable filters.

Discrimination of Facial Regions Based on Dynamic Grids of Point Representations

The application of an elastic graph-matching approach to discriminate facial image regions is presented. In contrast to the dynamic link architecture introduced by the Malsburg group, our application is not an identification task but a classification task. Therefore, our approach differs in several important aspects: (1) the choice of the filter set, (2) the selection of the positions of the nodes of the graph to represent the characteristic image information, (3) the generation of a representative reference pattern needed for the calculation of the classifications, and (4) a new two-step graph-matching approach based on the simulated annealing technique. The approach was tested on facial regions taking the eye region as an example target. A classification performance for the verification of eye regions of more than 93% was achieved.

Hierarchical filtering scheme for the detection of facial keypoints

Usually, the first processing step in computer vision systems consists of a spatial convolution with only a few simple filters. Therefore, information is lost of it is not represented explicitly for the following processing steps. This paper proposes a new hierarchical filter scheme that can efficiently synthesize the responses for a large number of specific filters. The scheme is based on steerable filters. It also allows for an efficient on-line adjustment of the trade off between the speed and the accuracy of the filters. We apply this method to the detection of facial keypoints, especially the eye corners. These anatomically defined keypoints exhibit a large variability in their corresponding image structures so that a flexible low level feature extraction is required.

Detektion und Verifikation von charakteristischen Bildpunkten in Gesichtsbildern

In diesem Beitrag wird ein Verfahren vorgestellt, welches automatisch charakteristische Punkte in Bildausschnitten aus Gesichtsbildern detektiert. Die Lokalisierung der Punkte basiert auf einer modellgesteuerten Detektion und Verfolgung der vorhandenen Linien und Kanten. Zur robusten Verfolgung der Kanten wird ein steuerbares Filterschema eingesetzt. Eine anschliesende Verifikation der detektierten Bildpunkte verringert zusatzlich die Wahrscheinlichkeit einer Falschdetektion. Die Extraktion der benotigten Informationen und Merkmale basiert sowohl fur die Detektion als auch fur die anschliesende Verifikation auf demselben Satz von Filtern. Zur Verifikation der gefundenen Bildpositionen wird eine Dynamische Zellstruktur (DCS-Netzwerk) verwendet, die durch ein uberwachtes Lernverfahren trainiert wird.

A common framework for preattentive and attentive vision using steerable filters

Motivated by human eye movement strategies a computer based ’attentional mechanism’ for processing face images is developed that comprises preattentive and attentive processing strategies. Both parts use a common filtering framework based on steerable filters. During the preattentive processing, prominent facial regions like the eye or the mouth region are localized. The selected regions are analyzed in more detail in the attentive processing step. A variety of complex features are derived applying an efficient filter method based on steerable filters.

Facial analysis applying an attentive processing strategy

A technical realization of an attentional mechanism localizing and analyzing prominent facial regions in gray-level images is presented. By adopting the gaze control principles of the HVS for developing an image processing system, a complex image analysis problem may be decomposed into a number of subproblems which can be solved step by step in a simpler way. The attentive processing strategy starts with the localization of the prominent facial regions based on a saliency representation carrying all the information needed to select and restrict the extent of the prominent facial regions. Subsequently, the detected and now spatially limited regions are classified to evaluate the benefit of applying more detailed analysis methods. During the detailed analysis step the exact positions of anatomical landmarks or keypoints such as eye and mouth centers are determined. Fundamental to the attentive processing strategy is that all processing modules consider and process only prominent and really characteristic image structures. Only those images structures are considered which contribute to the solution of the actual processing task. The attentive processing strategy proposed is able to cope with variations due to the perspective angle or pose, orientation, illumination, and contrast of the studied facial images.