Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Abstract

Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

“…Treating the Inception V3 classification results as ground truths, the Fowlkes-Mallows Index (FMI) was used to quantify the success of our k-means classifier in successfully defining clusters that are similar to the ground truth set of classes. The FMI is the geometric mean of the precision and recall that makes no assumption about the cluster structure 29 , 30 . Using this approach, we obtained an FMI score of 0.5, which indicates good similarity in the classification accuracies.…”

On the Search for Potentially Anomalous Traces of Cosmic Ray Particles in Images Acquired by Cmos Detectors for a Continuous Stream of Emerging Observational Data

“…Image aligning might improve results of further image analysis [ 102 , 103 , 104 , 105 ]. In case of CREDO dataset, the aligning is based on translating images so that the pixels with the highest grayscale intensity will be in the center of the image, and rotating images so that the brightest collinear pixels will be horizontal.…”

IoT Devices Authentication Using Artificial Neural Network

“…In unsupervised learning algorithms, the training of the network is done first then features are gained from internal data. Some reinforcement algorithms are also used that combine both supervised and unsupervised techniques, wherein the weights for the network are raised or reduced for honor or penalty [33]. A popular method for training artificial neural network is the back-propagation algorithm [34].…”