A Community Detection Approach to Cleaning Extremely Large Face Database

Though it has been easier to build large face datasets by collecting images from the Internet in this Big Data era, the time-consuming manual annotation process prevents researchers from constructing larger ones, which makes the automatic cleaning of noisy labels highly desirable. However, identifying mislabeled faces by machine is quite challenging because the diversity of a person’s face images that are captured wildly at all ages is extraordinarily rich. In view of this, we propose a graph-based cleaning method that mainly employs the community detection algorithm and deep CNN models to delete mislabeled images. As the diversity of faces is preserved in multiple large communities, our cleaning results have both high cleanness and rich data diversity. With our method, we clean the extremely large MS-Celeb-1M face dataset (approximately 10 million images with noisy labels) and obtain a clean version of it called C-MS-Celeb (6,464,018 images of 94,682 celebrities). By training a single-net model using our C-MS-Celeb dataset, without fine-tuning, we achieve 99.67% at Equal Error Rate on the LFW face recognition benchmark, which is comparable to other state-of-the-art results. This demonstrates the data cleaning positive effects on the model training. To the best of our knowledge, our C-MS-Celeb is the largest clean face dataset that is publicly available so far, which will benefit face recognition researchers

Issues in Computer Vision Data Collection: Bias, Consent, and Label Taxonomy

Recent success of the convolutional neural network in image classification has pushed the computer vision community towards data-rich methods of deep learning. As a consequence of this shift, the data collection process has had to adapt, becoming increasingly automated and efficient to satisfy algorithms that require massive amounts of data. In the push for more data, however, careful consideration into decisions and assumptions in the data collection process have been neglected. Likewise, users accept datasets and their embed- ded assumptions at face-value, employing them in theory and application papers without scrutiny. As a result, undesirable biases, non-consensual data collection, and inappropriate label taxonomies are rife in computer vision datasets. This work aims to explore issues of bias, consent, and label taxonomy in computer vision through novel investigations into widely-used datasets in image classification, face recognition, and facial expression recognition. Through this work, I aim to challenge researchers to reconsider normative data collection and use practices such that computer vision systems can be developed in a more thoughtful and responsible manner