Figaro, hair detection and segmentation in the wild

Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.

[1]  Dragomir Anguelov,et al.  Markov random field models for hair and face segmentation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[2]  Andrew Zisserman,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Larry S. Davis,et al.  Detection and analysis of hair , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Edward H. Adelson,et al.  Recognizing Materials Using Perceptually Inspired Features , 2013, International Journal of Computer Vision.

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

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

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

[9]  Dan Wang,et al.  A novel two-tier Bayesian based method for hair segmentation , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[10]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, AMFG.

[11]  Noah Snavely,et al.  OpenSurfaces , 2013, ACM Trans. Graph..

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

[13]  Pierre-Yves Coulon,et al.  Frequential and color analysis for hair mask segmentation , 2008, 2008 15th IEEE International Conference on Image Processing.

[14]  ZissermanAndrew,et al.  A Statistical Approach to Material Classification Using Image Patch Exemplars , 2009 .

[15]  Victoria Sherrow,et al.  Encyclopedia of hair : a cultural history , 2006 .

[16]  Iasonas Kokkinos,et al.  Describing Textures in the Wild , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Dan Wang,et al.  A novel coarse-to-fine hair segmentation method , 2011, Face and Gesture 2011.

[18]  Hatice Gunes,et al.  An accurate algorithm for head detection based on XYZ and HSV hair and skin color models , 2008, 2008 15th IEEE International Conference on Image Processing.

[19]  Eam Khwang Teoh,et al.  Human Hair Segmentation and Length Detection for Human Appearance Model , 2014, 2014 22nd International Conference on Pattern Recognition.

[20]  Dan Wang,et al.  Isomorphic Manifold Inference for hair segmentation , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[21]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[22]  Ko Nishino,et al.  Visual Material Traits: Recognizing Per-Pixel Material Context , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[23]  Tetsunori Kobayashi,et al.  A method of gender classification by integrating facial, hairstyle, and clothing images , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[24]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[25]  Riccardo Leonardi,et al.  Over-the-shoulder shot detection in art films , 2015, 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI).

[26]  Massimo Mauro,et al.  Multi-class semantic segmentation of faces , 2015, 2015 IEEE International Conference on Image Processing (ICIP).