Semi-Supervised Face Detection

This paper presents a discussion on semi-supervised learning of probabilistic mixture model classifiers for face detection. We present a theoretical analysis of semi-supervised learning and show that there is an overlooked fundamental difference between the purely supervised and the semisupervised learning paradigms. While in the supervised case, increasing the amount of labeled training data is always seen as a way to improve the classifier’s performance, the converse might also be true as the number of unlabeled data is increased in the semi-supervised case. We also study the impact of this theoretical finding on Bayesian network classifiers, with the goal of avoiding the performance degradation with unlabeled data. We apply the semisupervised approach to face detection and we show that learning the structure of Bayesian network classifiers enables learning good classifiers for face detection with a small labeled set and a large unlabeled set.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Bin Shen,et al.  Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers , 2002, Machine Learning.

[3]  Abbas Z. Kouzani,et al.  Locating human faces within images , 2003, Comput. Vis. Image Underst..

[4]  László Györfi,et al.  A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.

[5]  Fabio Gagliardi Cozman,et al.  Semi-supervised Learning of Classifiers : Theory , Algorithms and Their Application to Human-Computer Interaction , 2004 .

[6]  Nicu Sebe,et al.  Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[8]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  Narendra Ahuja,et al.  A SNoW-Based Face Detector , 1999, NIPS.

[10]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Rayid Ghani,et al.  Combining Labeled and Unlabeled Data for MultiClass Text Categorization , 2002, ICML.

[13]  Shumeet Baluja,et al.  Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data , 1998, NIPS.

[14]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[15]  Henry Schneiderman,et al.  Learning a restricted Bayesian network for object detection , 2004, CVPR 2004.

[16]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[17]  David A. Landgrebe,et al.  The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon , 1994, IEEE Trans. Geosci. Remote. Sens..

[18]  Andrew McCallum,et al.  Employing EM and Pool-Based Active Learning for Text Classification , 1998, ICML.

[19]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[20]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[21]  Thomas S. Huang,et al.  Generative and discriminative face modelling for detection , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[22]  Jerome H. Friedman,et al.  On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.

[23]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Adrian Corduneanu,et al.  Continuation Methods for Mixing Heterogenous Sources , 2002, UAI.

[25]  Terence J. O'Neill.  Normal Discrimination with Unclassified Observations , 1978 .

[26]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[27]  David A. Bell,et al.  Learning Bayesian networks from data: An information-theory based approach , 2002, Artif. Intell..

[28]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Fabio Gagliardi Cozman,et al.  Semi-Supervised Learning of Mixture Models , 2003, ICML.

[30]  Ron Kohavi,et al.  Supervised and Unsupervised Discretization of Continuous Features , 1995, ICML.

[31]  T. Cover,et al.  The relative value of labeled and unlabeled samples in pattern recognition , 1993, Proceedings. IEEE International Symposium on Information Theory.

[32]  Tong Zhang,et al.  The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.

[33]  M. Seeger.  Learning with labeled and unlabeled dataMatthias , 2001 .