Pontificia Universidad Católica de Chile Pontificia Universidad Católica de Chile
Mery, D.; Bowyer K. (2014): Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches, Workshop on Soft Biometrics in conjunction with European Conference on Computer Vision (ECCV 2014), Zurich, Sep. 7th. (Best Paper Award) (2014)

Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches

Revista : Computer Vision - ECCV 2014 Workshops
Volumen : 8926
Páginas : 778-792
Tipo de publicación : Conferencia No DCC Ir a publicación


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 -like 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+). 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. In the testing stage, random test patches of the query image are extracted, and for each test patch a dictionary is built concatenating the `best’ representative dictionary of each class. Using this adapted dictionary, each 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 seven face databases in order to recognize facial expression, gender, race and disguise. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.