Facial Attribute Recognition: A Survey

Thom, Nathan

doi:10.1007/978-3-030-03243-2_815-1

Cited by 13 publications

(14 citation statements)

References 41 publications

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“…They found that the cosine similarity between images can be used to improve face recognition under nonideal conditions. Past research has also shown that soft biometrics, such as the use of prominent facial features or hairstyle, can be used to improve facial recognition technology [31,39].…”

Section: Caricatures In Computer Sciencementioning

confidence: 99%

Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems

Davis¹

2023

Intelligent Video Surveillance - New Perspectives

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Advances in computer vision have been primarily motivated by a better understanding of how humans perceive and codify faces. Broadly speaking, progress made in the fields of face recognition and identification has been strongly influenced by the biological mechanisms identified by research in the field of cognitive psychology. Research in cognitive psychology has long acknowledged that human face recognition and identification rely heavily on prominent features and that caricatures are capable of modeling prominent features in a multitude of ways. The field of computer science has done little to no research in the area of application of prominent features to recognition systems. This chapter discusses existing caricature research in cognitive psychology and computer vision, current issues with the practical application of caricatures to face recognition in computer vision, and how caricatures can be used to improve existing surveillance systems.

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Section: Caricatures In Computer Sciencementioning

confidence: 99%

Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems

Davis¹

2023

Intelligent Video Surveillance - New Perspectives

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“…State-of-the-art methods employ deep CNNs, with a focus on using smaller models and training directly from attribute data [ 19 , 31 , 32 , 33 ]. [ 34 ] provides a thorough review of facial attribute recognition since its inception. Facial attributes are directly related to social traits and first impressions, as has been shown in the psychology research on the topic.…”

Section: Related Workmentioning

confidence: 99%

“…Ref. [34] provides a thorough review of facial attribute recognition since its inception. Facial attributes are directly related to social traits and first impressions, as has been shown in the psychology research on the topic.…”

Section: Facial Attributesmentioning

confidence: 99%

The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

Farlessyost

Grant

Davis

et al. 2021

Sensors

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First impressions make up an integral part of our interactions with other humans by providing an instantaneous judgment of the trustworthiness, dominance and attractiveness of an individual prior to engaging in any other form of interaction. Unfortunately, this can lead to unintentional bias in situations that have serious consequences, whether it be in judicial proceedings, career advancement, or politics. The ability to automatically recognize social traits presents a number of highly useful applications: from minimizing bias in social interactions to providing insight into how our own facial attributes are interpreted by others. However, while first impressions are well-studied in the field of psychology, automated methods for predicting social traits are largely non-existent. In this work, we demonstrate the feasibility of two automated approaches—multi-label classification (MLC) and multi-output regression (MOR)—for first impression recognition from faces. We demonstrate that both approaches are able to predict social traits with better than chance accuracy, but there is still significant room for improvement. We evaluate ethical concerns and detail application areas for future work in this direction.

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“…Recently, a large number of deep learning-based PAR methods [7]- [11] have been developed and have shown promising performance. These methods typically formulate the problem of predicting attributes as the problem of designing proper deep neural networks on the training datasets.…”

Section: Introductionmentioning

confidence: 99%

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Yan

Xue

et al. 2023

IEEE Trans. Cybern.

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Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisylabeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods.

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Facial Attribute Recognition: A Survey

Cited by 13 publications

References 41 publications

Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems

Improving Face Recognition Using Artistic Interpretations of Prominent Features: Leveraging Caricatures in Modern Surveillance Systems

The Effectiveness of Multi-Label Classification and Multi-Output Regression in Social Trait Recognition

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

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