Person recognition for service robotics applications

The acceptance of service robots comes along with the ability to adapt to user specific preferences. This requires that a robot can determine the identity of the user. As for humans, robust user recognition is based on the identification of the face. However, despite the plethora of published work on face recognition that is robust against real world noise such a illumination, head alignment or facial expressions there is no robust off-the-shelf non-commercial software available to be used in typical robotics applications. Hence, this paper introduces a ready-to-use open-source ROS package providing a face detection and identification system that is comprising novel and state-of-the-art solutions to various aspects of face recognition while utilizing modern RGB-D sensors. This work demonstrates a solution for face recognition in robotic settings that is robust against varying illumination, gaze directions of the head, and facial expressions while operating with online performance. The paper provides a thorough evaluation of the face recognition system based on standard database tests and on real world scenarios regarding these criteria.

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

[2]  Gary R. Bradski,et al.  Learning OpenCV - computer vision with the OpenCV library: software that sees , 2008 .

[3]  Alexander Verl,et al.  Face Detection using 3-D Time-of-Flight and Colour Cameras , 2010, ISR/ROBOTIK.

[4]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Dong Xu,et al.  Face Verification With Balanced Thresholds , 2007, IEEE Transactions on Image Processing.

[7]  Xudong Jiang,et al.  Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Michael G. Strintzis,et al.  Use of depth and colour eigenfaces for face recognition , 2003, Pattern Recognit. Lett..

[9]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Tao Wang,et al.  Face detection using SURF cascade , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[11]  Meng Joo Er,et al.  Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[13]  Jintao Li,et al.  A Novel Method to Compensate Variety of Illumination in Face Detection , 2002, JCIS.

[14]  Erik G. Learned-Miller,et al.  Online domain adaptation of a pre-trained cascade of classifiers , 2011, CVPR 2011.

[15]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Hossein Mobahi,et al.  Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[18]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[19]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Naomi Inoue,et al.  Model free head pose estimation using stereovision , 2012, Pattern Recognit..

[21]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[23]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[24]  R I Hg,et al.  An RGB-D Database Using Microsoft's Kinect for Windows for Face Detection , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[25]  Qiu-Qi Ruan,et al.  Face Recognition Using L-Fisherfaces , 2010, J. Inf. Sci. Eng..

[26]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Goel Tripti,et al.  Comparative Analysis of various Illumination Normalization Techniques for Face Recognition , 2011 .

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

[29]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.