Sense beauty via face, dressing, and/or voice
Proceedings of the 20th ACM international conference on Multimedia
Hi, magic closet, tell me what to wear!
Proceedings of the 20th ACM international conference on Multimedia
Hi, magic closet, tell me what to wear!
Proceedings of the 20th ACM international conference on Multimedia
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Attributes for classifier feedback
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Describing clothing by semantic attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Discovering a lexicon of parts and attributes
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Relative forest for attribute prediction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Efficient clothing retrieval with semantic-preserving visual phrases
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Qualitative pose estimation by discriminative deformable part models
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Towards decrypting attractiveness via multi-modality cues
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Spatial Recurrences for Pedestrian Classification
Journal of Mathematical Imaging and Vision
A convolutional neural network for pedestrian gender recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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We propose a method for recognizing attributes, such as the gender, hair style and types of clothes of people under large variation in viewpoint, pose, articulation and occlusion typical of personal photo album images. Robust attribute classifiers under such conditions must be invariant to pose, but inferring the pose in itself is a challenging problem. We use a part-based approach based on poselets. Our parts implicitly decompose the aspect (the pose and viewpoint). We train attribute classifiers for each such aspect and we combine them together in a discriminative model. We propose a new dataset of 8000 people with annotated attributes. Our method performs very well on this dataset, significantly outperforming a baseline built on the spatial pyramid match kernel method. On gender recognition we outperform a commercial face recognition system.