Abstract
This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.
Highlights
IntroductionBiometrics have attracted much interest in the last decade, for the high number of applications they can have in industrial and academic fields [1]
Biometrics have attracted much interest in the last decade, for the high number of applications they can have in industrial and academic fields [1].Recently, soft biometrics have been clearly defined in [2] as a set of traits providing information about an individual, even though the lack of distinctiveness and permanence does not allow to authenticate individuals
This paper investigates local binary pattern (LBP) [7, 8], compound local binary pattern (CLBP) [9], histogram of oriented gradient (HOG) [10], and one descriptor that represents a trade-off between the two aspects: Weber local descriptor (WLD) [11]
Summary
Biometrics have attracted much interest in the last decade, for the high number of applications they can have in industrial and academic fields [1]. Soft biometrics have been clearly defined in [2] as a set of traits providing information about an individual, even though the lack of distinctiveness and permanence does not allow to authenticate individuals. These traits can be either continuous (e.g., height and weight) or discrete (e.g., gender, eye color, ethnicity, etc.). In the last few decades, computer vision, as well as other information science fields, have largely investigated the problem of the automatic estimation of the main soft Even socially assistive technologies are a new and emerging field where these solutions could considerably improve the overall human-machine interaction level (for example with autistic individuals [3], as well as with people with dementia [4] and, generally, for elderly care [5]).
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