Improving face gender classification by adding deliberately misaligned faces to the training data

2008 23rd International Conference Image and Vision Computing New Zealand, IVCNZ

Volumn , Issue , 2008, Pages

  Mayo, M ^a   Zhang, E ^a  

^a UNIVERSITY OF WAIKATO   (New Zealand)

Author keywords

Face alignment; Face classification; Face detection; Gender classification; Image classification; Local binary pattern; Machine learning; SIFT keypoints; Spatial pyramid; Support vector machines

Indexed keywords

ALIGNMENT; CLASSIFIERS; CONTENT BASED RETRIEVAL; IMAGE ANALYSIS; IMAGE CLASSIFICATION; ROBOT LEARNING; STATISTICAL TESTS; SUPPORT VECTOR MACHINES;

FACE ALIGNMENT; FACE CLASSIFICATION; FACE DETECTION; GENDER CLASSIFICATION; LOCAL BINARY PATTERN; MACHINE LEARNING; SIFT KEYPOINTS; SPATIAL PYRAMID;

FACE RECOGNITION;

EID: 63049138724     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVCNZ.2008.4762066     Document Type: Conference Paper

References (14)

1. Viola P. & Jones M. 2001. Rapid Object Detection Using a Boosted Cascade of Simple Features. In Proc IEEE CS Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 511-518.
2. Mäkinen E. & Raisamo R. 2008. Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(3) pp. 541-547.
3. Wu Y., et. al. 2007. Robust Speaking Face Identification for Video Analysis. In. Advances in Multimedia Information Processing - PCM 2007, Lecture Notes in Computer Science 4810, Springer, pp. 665-674.
4. Cootes T. & Taylor C. 2001. Statistical Models of Appearance for Medical Image Analysis and Computer Vision.
5. Phillips P., Weschler H., Huang J., Rauss P. 1998. The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5) pp. 295-306.
6. Phillips P., Moon H., Rizvi S., & Rauss P. 2000. The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence, 22, v. 1090-1104.
7. Lowe D. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), pp. 91-110.
8. Mäenpää T & Pietikäinen M. 2005. Texture analysis with local binary patterns. In Chen C. & Wang P. (eds) Handbook of Pattern Recognition and Computer Vision, 3rd ed, World Scientific, 197-216
9. Ahonen T., Hadid A. & Pietikäinen M. 2006. Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. on Pattern Recognition and Machine Intelligence 28(12) 2037-41.
10. Lazebnik S., Schmid C. & Ponce J. 2006. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In Proc. of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) Volume 2, pp. 2169-2178.
11. Witten I. and Frank E. 2005. Data Mining: Practical machine learning tools and techniques (2nd Edition). Morgan Kaufmann, San Francisco.
12. Platt J. 1998. Machines using Sequential Minimal Optimization. In Schoelkopf B., Burges C. and Smola A. (Eds.) Advances in Kernel Methods - Support Vector Learning.
13. Keerthi S., et al. 1999. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation 13(3) 637-649.
14. Breiman, L. 2001. Random Forests. Machine Learning 45 (1):5-32.