Authors:
Oscar Déniz
1
;
Gloria Bueno
1
;
Jesus Salido
2
and
Fernando de la Torre
3
Affiliations:
1
E.T.S. Ingenieros Industriales, Spain
;
2
E. Superior de Informática, Spain
;
3
Carnegie Mellon University, United States
Keyword(s):
Face recognition, Histogram of oriented gradients.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Histograms of Oriented Gradients have been recently used as discriminating features for face recognition. In this work we improve on that work in a number of aspects. As a first contribution, it identifies the necessity of performing feature selection or transformation, especially if HOG features are extracted from overlapping cells. Second, the use of four different face databases allowed us to conclude that, if HOG features are extracted from facial landmarks, the error of landmark localization plays a crucial role in the absolute recognition rates achievable. This implies that the recognition rates can be lower for easier databases if landmark localization is not well adapted to them. This prompted us to extract the features from a regular grid covering the whole image. Overall, these considerations allow to obtain a significant recognition rate increase (up to 10% in some subsets) on the standard FERET database with respect to previous work.