Human‐level face verification with intra‐personal factor analysis and deep face representation

Abstract

The last two decades have seen an escalating interest in methods for large-scale unconstrained face recognition. While the promise of computer vision systems to efficiently and accurately verify and identify faces in naturally occurring circumstances still remains elusive, recent advances in deep learning are taking us closer to human-level recognition. In this study, the authors propose a new paradigm which employs deep features in a feature extractor and intra-personal factor analysis as a recogniser. The proposed new strategy represents the face changes of a person using identity specific components and the intra-personal variation through reinterpretation of a Bayesian generative factor analysis model. The authors employ the expectation-maximisation algorithm to calculate model parameters which cannot be observed directly. Recognition outcomes achieved through benchmarking on large-scale wild databases, Labeled Faces in the Wild (LFW) and Youtube Face (YTF), clearly prove that the proposed approach provides remarkable face verification performance improvement over state-of-the-art approaches.

Boosting the Face Recognition Performance of Ensemble Based LDA for Pose, Non-uniform Illuminations, and Low-Resolution Images

“…However, the potential of computer vision algorithms to efficiently, reliably, and accurately identify human faces in naturally occurring circumstances still remains elusive. Recent trends in deep learning are taking us closer to human-level recognition [39]- [40]. In this study, we empirically observed that deep learning based face recognition algorithm [39], on high image resolutions, such as 80×80 pixels and above outperforms compared algorithms on the LFW and Multi-PIE databases for pose variation and under non-uniform illuminations.…”

The Influence of Self-Esteem, Perceived Stress and Perceived on Internet Addiction During Pandemic Covid-19

“…Craig (1995), detailed that individuals who hold negative assessments about themselves utilize addictive substances to escape or pull back from their low self-beliefs. Study by Munasinghe et al (2018), respondents who fill the Internet addiction test, meet the criteria for Internet addiction significantly have low self-esteem.…”

Feature Transformation Framework for Enhancing Compactness and Separability of Data Points in Feature Space for Small Datasets

“…State-of-the-art deep learning algorithms have achieved near-human-level accuracy in applications such as computer vision [24][25][26][27][28]. Nevertheless, one challenge remains for these algorithms when faced with inadequate amounts in acquiring adequate amounts of labeled data [29].…”