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![Exploratory investigation to learn what facial features in a dense model can be matched with what is available in a sparse probe. (a) A dense 3D model of a human face used in the gallery. (b) 2D features extracted from a surveillance video frame. (c) Features tracked in several frames like (b) to generate a sparse 3D probe face [3].](profile/Hamparsum-Bozdogan/publication/221121707/figure/fig2/AS:667654306136071@1536192599680/Exploratory-investigation-to-learn-what-facial-features-in-a-dense-model-can-be-matched.png)
Exploratory investigation to learn what facial features in a dense model can be matched with what is available in a sparse probe. (a) A dense 3D model of a human face used in the gallery. (b) 2D features extracted from a surveillance video frame. (c) Features tracked in several frames like (b) to generate a sparse 3D probe face [3].
Source publication
In this paper, we derive a data mining framework to analyze 3D features on human faces. The framework leverages kernel density estimators, genetic algorithm and an information complexity criterion to identify discriminant feature-clusters of lower dimensionality. We apply this framework on human face anthropometry data of 32 features collected from...
Context in source publication
Context 1
... in real world situations, we often cannot expect co-operation for acquiring dense 3D probe models. The question then that arises in applications where sparse 3D points and feature probes are extracted from surveillance videos [3] as shown in Figure 1 or by other forensic clinical means, is what geometric features in a human face should be extracted and matched with the dense gallery data for reliable face identification. ...
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Citations
Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.
