Distributed object recognition via feature unmixing
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Age classification for pose variant and occluded faces
Proceedings of the international conference on Multimedia
Multi-view gender classification using hierarchical classifiers structure
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Human attributes from 3D pose tracking
Computer Vision and Image Understanding
Gender identification using feature patch-based bayesian classifier
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Bag of features using sparse coding for gender classification
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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This paper presents a novel framework for detecting, localizing, and classifying faces in terms of visual traits, e.g., sex or age, from arbitrary viewpoints and in the presence of occlusion. All three tasks are embedded in a general viewpoint-invariant model of object class appearance derived from local scale-invariant features, where features are probabilistically quantified in terms of their occurrence, appearance, geometry, and association with visual traits of interest. An appearance model is first learned for the object class, after which a Bayesian classifier is trained to identify the model features indicative of visual traits. The framework can be applied in realistic scenarios in the presence of viewpoint changes and partial occlusion, unlike other techniques assuming data that are single viewpoint, upright, prealigned, and cropped from background distraction. Experimentation establishes the first result for sex classification from arbitrary viewpoints, an equal error rate of 16.3 percent, based on the color FERET database. The method is also shown to work robustly on faces in cluttered imagery from the CMU profile database. A comparison with the geometry-free bag-of-words model shows that geometrical information provided by our framework improves classification. A comparison with support vector machines demonstrates that Bayesian classification results in superior performance.