This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In\nparticular, a recently introduced common framework is the starting point of the study: it includes an initial facial\ndetection, the subsequent facial traits description, the data reduction step, and the final classification step. The\nalgorithmic configurations are featured by different descriptors and different strategies to build the training dataset\nand to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available\ndatasets and image sequences specifically acquired in order to evaluate the performance even under real-world\nconditions, i.e., in the presence of scaling and rotation.
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