This paper presents an innovative approach for the detection of faces in three dimensional scenes. The method is tolerant against partial occlusions produced by the presence of any kind of object. The detection algorithm uses invariant properties of the surfaces to segment salient facial features, namely the eyes and the nose. At least two facial features must be clearly visible in order to perform face detection. Candidate faces are then registered using an ICP (Iterative Correspondent Point) based approach aimed to avoid those samples which belong to the occluding objects. The final face versus non-face discrimination is computed by a Gappy PCA (GPCA) classifier which is able to classify candidate faces using only those regions of the surface which are considered to be non-occluded. The algorithm has been tested using the UND database obtaining 100% of correct detection and only one false alarm. The database has been then processed with an artificial occlusions generator producing realistic acquisitions that emulate unconstrained scenarios. A rate of 89.8% of correct detections shows that 3D data is particularly suited for handling occluding objects. The results have been also verified on a small test set containing real world occlusions obtaining 90.4% of correctly detected faces. The proposed approach can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios. © 2009 Springer Science+Business Media, LLC.

Schettini, R., Cusano, C., Colombo, A. (2009). Gappy PCA classification for occlusion tolerant 3D face detection. JOURNAL OF MATHEMATICAL IMAGING AND VISION, 35(3), 193-207 [10.1007/s10851-009-0165-y].

Gappy PCA classification for occlusion tolerant 3D face detection

SCHETTINI, RAIMONDO;CUSANO, CLAUDIO;COLOMBO, ALESSANDRO
2009

Abstract

This paper presents an innovative approach for the detection of faces in three dimensional scenes. The method is tolerant against partial occlusions produced by the presence of any kind of object. The detection algorithm uses invariant properties of the surfaces to segment salient facial features, namely the eyes and the nose. At least two facial features must be clearly visible in order to perform face detection. Candidate faces are then registered using an ICP (Iterative Correspondent Point) based approach aimed to avoid those samples which belong to the occluding objects. The final face versus non-face discrimination is computed by a Gappy PCA (GPCA) classifier which is able to classify candidate faces using only those regions of the surface which are considered to be non-occluded. The algorithm has been tested using the UND database obtaining 100% of correct detection and only one false alarm. The database has been then processed with an artificial occlusions generator producing realistic acquisitions that emulate unconstrained scenarios. A rate of 89.8% of correct detections shows that 3D data is particularly suited for handling occluding objects. The results have been also verified on a small test set containing real world occlusions obtaining 90.4% of correctly detected faces. The proposed approach can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios. © 2009 Springer Science+Business Media, LLC.
Articolo in rivista - Articolo scientifico
gappy pca, 3d face detection
English
2009
35
3
193
207
none
Schettini, R., Cusano, C., Colombo, A. (2009). Gappy PCA classification for occlusion tolerant 3D face detection. JOURNAL OF MATHEMATICAL IMAGING AND VISION, 35(3), 193-207 [10.1007/s10851-009-0165-y].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/16117
Citazioni
  • Scopus 32
  • ???jsp.display-item.citation.isi??? 26
Social impact