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Abstract-Soft biometric information extracted from a human body (e.g., height, gender, skin color, hair color, and so on) is ancillary information easily distinguished at a distance but it is not fully distinctive by itself in recognition tasks. However, this soft information can be explicitly fused with biometric recognition systems to improve the overall recognition when confronting high variability conditions. One significant example is visual surveillance, where face images are usually captured in poor quality conditions with high variability and automatic face recognition systems do not work properly. In this scenario, the soft biometric information can provide very valuable information for person recognition. This paper presents an experimental study of the benefits of soft biometric labels as ancillary information based on the description of human physical features to improve challenging person recognition scenarios at a distance. In addition, we analyze the available soft biometric information in scenarios of varying distance between camera and subject. Experimental results based on the Southampton multibiometric tunnel database show that the use of soft biometric traits is able to improve the performance of face recognition based on sparse representation on real and ideal scenarios by adaptive fusion rules.

This work proposes a new neural network feature representation that help to leave out sensitive information in the decision-making process of pattern recognition and machine learning algorithms. The aim of this work is to develop a learning method capable to remove certain information from the feature space without drop of performance in a recognition task based on that feature space. Our work is in part motivated by the new international regulation for personal data protection, which forces data controllers to avoid discriminative hazards while managing sensitive data of users. Our method is based on a triplet loss learning generalization that introduces a sensitive information removal process. The method is evaluated on face recognition technologies using state-of-the-art algorithms and publicly available benchmarks. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins. The dataset includes more than 120K images from 24K identities with variety of poses, image quality, facial expressions, and illumination. The experiments demonstrate that it is possible to reduce sensitive information such as gender or ethnicity in the feature representation while retaining competitive performance in a face recognition task.

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The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard and moustache. We consider two assumptions: i) manual estimation of soft biometrics, and ii) automatic estimation from two Commercial Off-The-Shelf systems (COTS). All experiments are reported using the LFW database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores.