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reliable crowdsourcing

Reliable crowdsourcing is a distributed data collection and annotation approach that incorporates systematic quality-control mechanisms to produce accurate, high-quality ground-truth data from a diverse and potentially noisy pool of human contributors. Because individual crowd workers often exhibit varying degrees of expertise, attention, or subjective bias, this method relies on strategies such as redundant multi-worker task assignment, consensus modeling, and statistical or algorithmic filtering, such as expectation-maximization, to identify and discard unreliable annotations. By aggregating repeated inputs and mitigating noise, reliable crowdsourcing establishes consistent, high-fidelity datasets essential for training and evaluating machine learning and artificial intelligence systems.

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Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild

Shan Li, Weihong Deng, Junping Du

OrganizationsBeijing University of Posts and Telecommunications

Why you should read this

Presents RAF-DB, a large-scale real-world facial expression database labeled through reliable crowdsourcing, alongside a deep locality-preserving CNN that significantly improves in-the-wild emotion recognition across basic and compound expressions.

Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. The benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.

Added

2026-09-25