Classifying range images of human faces with Hausdorff distance

Achermann, B.; Bunke, Horst

doi:10.1109/icpr.2000.906199

Cited by 48 publications

(29 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Medioni and Waupotitsch [1], Lu et al [13], [21], Wang et al [12], Chang et al [5], and Maurer et al [29] matched face surfaces by the iterative closest point (ICP) algorithm [3] or its modified versions. The Hausdorff distance was also used for holistic surface matching [16], [19], [20]. 4.…”

Section: Facial Curve-based This Kind Of Methods Extractsmentioning

confidence: 99%

Robust 3D Face Recognition by Local Shape Difference Boosting

Wang

Liu

Tang

2010

IEEE Trans. Pattern Anal. Mach. Intell.

162

View full text Add to dashboard Cite

Abstract-This paper proposes a new 3D face recognition approach, Collective Shape Difference Classifier (CSDC), to meet practical application requirements, i.e., high recognition performance, high computational efficiency, and easy implementation. We first present a fast posture alignment method which is self-dependent and avoids the registration between an input face against every face in the gallery. Then, a Signed Shape Difference Map (SSDM) is computed between two aligned 3D faces as a mediate representation for the shape comparison. Based on the SSDMs, three kinds of features are used to encode both the local similarity and the change characteristics between facial shapes. The most discriminative local features are selected optimally by boosting and trained as weak classifiers for assembling three collective strong classifiers, namely, CSDCs with respect to the three kinds of features. Different schemes are designed for verification and identification to pursue high performance in both recognition and computation. The experiments, carried out on FRGC v2 with the standard protocol, yield three verification rates all better than 97.9 percent with the FAR of 0.1 percent and rank-1 recognition rates above 98 percent. Each recognition against a gallery with 1,000 faces only takes about 3.6 seconds. These experimental results demonstrate that our algorithm is not only effective but also time efficient.Index Terms-3D shape matching, collective shape difference classifier, face recognition, signed shape difference map.

show abstract

Section: Facial Curve-based This Kind Of Methods Extractsmentioning

confidence: 99%

Robust 3D Face Recognition by Local Shape Difference Boosting

Wang

Liu

Tang

2010

IEEE Trans. Pattern Anal. Mach. Intell.

162

View full text Add to dashboard Cite

show abstract

“…To this end, the symmetric Hausdorff distance [43] is used. The Hausdorff distance has been previously used to good effect with 3D meshes as a similarity measure for face recognition [44], [45]. Let S and S be the two facial meshes and ∂(v i , S ) be the distance between a vertex v i ∈ S and S .…”

Section: Landmark Localizationmentioning

confidence: 99%

3-D Face Detection, Landmark Localization, and Registration Using a Point Distribution Model

Nair

Cavallaro

2009

IEEE Trans. Multimedia

124

View full text Add to dashboard Cite

Abstract-We present an accurate and robust framework for detecting and segmenting faces, localizing landmarks and achieving fine registration of face meshes based on the fitting of a facial model. This model is based on a 3D Point Distribution Model (PDM) that is fitted without relying on texture, pose or orientation information. Fitting is initialized using candidate locations on the mesh, which are extracted from low-level curvaturebased feature maps. Face detection is performed by classifying the transformations between model points and candidate vertices based on the upper-bound of the deviation of the parameters from the mean model. Landmark localization is performed on the segmented face by finding the transformation that minimizes the deviation of the model from the mean shape. Face registration is obtained using prior anthropometric knowledge and the localized landmarks. The performance of face detection is evaluated on a database of faces and non-face objects where we achieve an accuracy of 99.6%. We also demonstrate face detection and segmentation on objects with different scale and pose. The robustness of landmark localization is evaluated with noisy data and by varying the number of shapes and model points used in the model learning phase. Finally, face registration is compared with the traditional Iterative Closest Point (ICP) method and evaluated through a face retrieval and recognition framework on the GavabDB dataset, where we achieve a recognition rate of 87.4% and a retreival rate of 83.9%.

show abstract

“…According to this experiment the best recognition rates were achieved using vertical profiles that pass through the central region of the face. Achermann (Achermann & Bunke, 2000) approached 3D face recognition based on an extension of Hausdorff distance matching. A database of 240 images were used, and 100% recognition rate was reported.…”

Section: Challengesmentioning

confidence: 99%