Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning

Abstract

In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters; our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.

Rockburst intensity prediction in underground buildings based on improved spectral clustering algorithm

“…As an important branch of clustering algorithms in unsupervised learning, spectral clustering algorithms have a low sensitivity to sample shapes, which tend to converge to the global optimal and support high-dimensional data (Bai et al, 2021). Therefore, it has been applied to various aspects, including pattern recognition during or before image processing (Shen et al, 2021;Guo et al, 2022), classification and prediction of big data samples (Pellicer-Valero et al, 2020;Wang and Shi, 2021), and segmentation of remote sensing images (Li et al, 2018). The application of spectral clustering has expanded in recent decades, which means that algorithms need to be tailored and improved in time to maintain usability and robustness in specific scenarios.…”