Why Is Facial Occlusion a Challenging Problem?

This paper investigates the main reason for the obtained low performance when the face recognition algorithms are tested on partially occluded face images. It has been observed that in the case of upper face occlusion, missing discriminative information due to occlusion only accounts for a very small part of the performance drop. The main factor is found to be the registration errors due to erroneous facial feature localization. It has been shown that by solving the misalignment problem, very high correct recognition rates can be achieved with a generic local appearance-based face recognition algorithm. In the case of a lower face occlusion, only a slight decrease in the performance is observed, when a local appearance-based face representation approach is used. This indicates the importance of local processing when dealing with partial face occlusion. Moreover, improved alignment increases the correct recognition rate also in the experiments against the lower face occlusion, which shows that face registration plays a key role on face recognition performance.

[1]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[2]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[3]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[4]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[5]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[6]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[7]  A. Martínez,et al.  The AR face databasae , 1998 .

[8]  Ralph Gross,et al.  Quo vadis Face Recognition , 2001 .

[9]  Marc Parizeau,et al.  Experiments on eigenfaces robustness , 2002, Object recognition supported by user interaction for service robots.

[10]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  B. V. K. Vijaya Kumar,et al.  A Still-to-Video Face Verification System Using Advanced Correlation Filters , 2004, ICBA.

[12]  Bülent Sankur,et al.  Feature selection in the independent component subspace for face recognition , 2004, Pattern Recognit. Lett..

[13]  Rainer Stiefelhagen,et al.  Local appearance based face recognition using discrete cosine transform , 2005, 2005 13th European Signal Processing Conference.

[14]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.

[17]  Sang Uk Lee,et al.  Face recognition using face-ARG matching , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Aristodemos Pnevmatikakis,et al.  Impact of Face Registration Errors on Recognition , 2006, AIAI.

[19]  Rainer Stiefelhagen,et al.  Analysis of Local Appearance-Based Face Recognition: Effects of Feature Selection and Feature Normalization , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[20]  Sanja Fidler,et al.  Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Aleix M. Martínez,et al.  Face recognition with occlusions in the training and testing sets , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[22]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[23]  Hazim Kemal Ekenel,et al.  A robust face recognition algorithm for real-world applications , 2009 .

[24]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.