Experimental Study of the Usefulness of External Face Features for Face Classification

Authors:
Àgata Lapedriza;David Masip;Jordi Vitrià
Affiliations:
Computer Vision Center-Dept. Ciències de la Computació, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, {agata, davidm,jordi}@cvc.uab.es;Computer Vision Center-Dept. Ciències de la Computació, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, {agata, davidm,jordi}@cvc.uab.es;Computer Vision Center-Dept. Ciències de la Computació, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, {agata, davidm,jordi}@cvc.uab.es
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

Citing 6
Cited 0

Learning Gender with Support Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Class-Specific, Top-Down Segmentation

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research
Boosted discriminant projections for nearest neighbor classification

Pattern Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

In general face classification problems, only the internal features of the face are commonly used, rejecting the information located at head, chin, and ears, since due to their variability is not easy to extract this information. In this paper, a complete scheme based on a Top-Down reconstruction algorithm to extract External Features of face images is proposed. We use the Non negative Matrix Factorization (NMF) algorithm to obtain the final coefficients that encode the external information and using this codification the faces are classified. Our experimental results in different face classification problems show that the information contributed by the external features is significant and useful for classification purposes.