Clustering Facial Attributes: Narrowing the Path From Soft to Hard Biometrics

Abate, Andrea F.; Barra, Paola; Barra, Silvio; Molinari, Cristiano; Nappi, Michele; Narducci, Fabio

doi:10.1109/access.2019.2962010

Cited by 23 publications

(14 citation statements)

References 32 publications

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“…As for the potential extension of this research, we consider the unsupervised clustering of facial attributes for preprocessing the constrained and unconstrained datasets, so that the learning for normalization can be made attributeoriented. The approach proposed in [32] can be a decent example.…”

Section: Discussionmentioning

confidence: 99%

Dual-View Normalization for Face Recognition

Hsu

Tang

2020

IEEE Access

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Face normalization refers to a family of approaches that rotate a non-frontal face to the frontal pose for better handling of face recognition. While a great majority of face normalization methods focus on frontal pose only, we proposed a framework for dual-view normalization that generates a frontal pose and an additional yaw-45 • pose to an input face of an arbitrary pose. The proposed Dual-View Normalization (DVN) framework is designed to learn the transformation from a source set to two normal sets. The source set contains faces collected in the wild and covers a wide scope of variables. One normal set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The other normal set contains faces also taken under controlled conditions and balanced in illumination, but in 45 • pose. The DVN framework is composed of one face encoder, two layers of generators, and two sets of discriminators. The encoder is made of a state-of-the-art face recognition network, which is not updated during training, and it acts as a facial feature extractor. The Layer-1 generators are trained on both the source and normal sets, aiming at learning the transformation from the source set to both normal sets. The trained generators can transform an arbitrary face into a pair of normalized faces, one in frontal pose and the other in 45 • pose. The Layer-2 generators are trained to enhance the identity preservation of the faces made by the Layer-1 generators by minimizing the cross-pose identity loss. The discriminators are trained to ensure the photo-realistic quality of the dual-view normalized face images generated by the generators. The loss functions employed in the generators and the discriminators are designed to achieve satisfactory dual-view normalization outcomes and identity preservation. We verify the DVN framework on benchmark databases and compare with other state-of-the-art approaches for tackling face recognition.

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Section: Discussionmentioning

confidence: 99%

Dual-View Normalization for Face Recognition

Hsu

Tang

2020

IEEE Access

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“…While demographic attributes are the most commonly studied [21], there are also studies on subject-specific attributes (e.g., hair style, expression and accessories) [22]- [25] and environmental context (e.g., illumination and resolution) [26]. In [24], Abate et al made a comparison of different clustering algorithms on soft-biometric data. Their goal was to show that soft-biometric attributes can be clustered to provide sets of similar-looking subjects, which might help identify suspects in the presence of a challenging environmental context (e.g., occlusion).…”

Section: A Effects Of Soft-biometric Attributes On Fvmentioning

confidence: 99%

Fair Face Verification by Using Non-Sensitive Soft-Biometric Attributes

2022

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Facial recognition has been shown to have different accuracy for different demographic groups. When setting a threshold to achieve a specific False Match Rate (FMR) on a mixed demographic impostor distribution, some demographic groups can experience a significantly worse FMR. To mitigate this, some authors have proposed to use demographic-specific thresholds. However, this can be impractical in an operational scenario, as it would either require users to report their demographic group or the system to predict the demographic group of each user. Both of these options can be deemed controversial since the demographic group is a sensitive attribute. Further, this approach requires listing the possible demographic groups, which can become controversial in itself. We show that a similar mitigation effect can be achieved using non-sensitive predicted soft-biometric attributes. These attributes are based on the appearance of the users (such as hairstyle, accessories, and facial geometry) rather than how the users self-identify. Our experiments use a set of 38 binary non-sensitive attributes from the MAAD-Face dataset. We report results on the Balanced Faces in the Wild dataset, which has a balanced number of identities by race and gender. We compare clustering-based and decision-tree-based strategies for selecting thresholds. We show that the proposed strategies can reduce differential outcomes in intersectional groups twice as effectively as using gender-specific thresholds and, in some cases, are also better than using race-specific thresholds.

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“…Zhao et al [28] proposed a lightweight expression detection model that can solve the delay problem under natural conditions. P. Barra et al [29] proposed a neural network model for face attributes recognition based on transfer learning to group faces according to common facial features.…”

Section: Related Workmentioning

confidence: 99%