Automatic facial attribute analysis via adaptive sparse representation of random patches. Mery, D. and Bowyer, K. Pattern Recognition Letters, 68:260-269, Elsevier, 2015.
Automatic facial attribute analysis via adaptive sparse representation of random patches [pdf]Paper  abstract   bibtex   
It is well known that some facial attributes -like soft biometric traits- can increase the performance of traditional biometric systems and help recognition based on human descriptions. In addition, other facial attributes, such as facial expressions, can be used in human?computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adaptive Sparse Representation of Random Patches (ASR+). The proposed method consists of two stages: In the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition -a two-class problem-, images of females/males) in order to construct representative dictionaries. A stop list is used to remove very common words of the dictionaries. In the testing stage, random test patches of the query image are extracted, and for each non-stopped test patch a dictionary is built concatenating the ?best? representative dictionary of each class. Using this adapted dictionary, each non?stopped test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on eight face databases in order to recognize facial expression, gender, race, disguise and beard. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.
@article{Mery2015:PLR,
  title={Automatic facial attribute analysis via adaptive sparse representation of random patches},
  author={Mery, Domingo and Bowyer, Kevin},
  journal={Pattern Recognition Letters},
  volume={68},
  pages={260-269},
  year={2015},
  publisher={Elsevier},
url = {http://dmery.sitios.ing.uc.cl/Prints/ISI-Journals/2015-PLR.pdf},
abstract = {It is well known that some facial attributes -like soft biometric traits- can increase the performance of traditional biometric systems and help recognition based on human descriptions. In addition, other facial attributes, such as facial expressions, can be used in human?computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adaptive Sparse Representation of Random Patches (ASR+). The proposed method consists of two stages: In the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition -a two-class problem-, images of females/males) in order to construct representative dictionaries. A stop list is used to remove very common words of the dictionaries. In the testing stage, random test patches of the query image are extracted, and for each non-stopped test patch a dictionary is built concatenating the ?best? representative dictionary of each class. Using this adapted dictionary, each non?stopped test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on eight face databases in order to recognize facial expression, gender, race, disguise and beard. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.}}
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