Robust Face Recognition Providing the Identity and its Reliability Degree Combining Sparse Representation and Multiple Features

For decades, face recognition (FR) has attracted a lot of attention, and several systems have been successfully developed to solve this problem. However, the issue deserves further research effort so as to reduce the still existing gap between the computer and human ability in solving it. Among the others, one of the human skills concerns his ability in naturally conferring a \u201cdegree of reliability\u201d to the face identification he carried out. We believe that providing a FR system with this feature would be of great help in real application contexts, making more flexible and treatable the identification process. In this spirit, we propose a completely automatic FR system robust to possible adverse illuminations and facial expression variations that provides together with the identity the corresponding degree of reliability. The method promotes sparse coding of multi-feature representations with LDA projections for dimensionality reduction, and uses a multistage classifier. The method has been evaluated in the challenging condition of having few (3\u20135) images per subject in the gallery. Extended experiments on several challenging databases (frontal faces of Extended YaleB, BANCA, FRGC v2.0, and frontal faces of Multi-PIE) show that our method outperforms several state-of-the-art sparse coding FR systems, thus demonstrating its effectiveness and generalizability

Multi-classifier ensemble based on dynamic weights

In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the real-time importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified

Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM Computing Surveys (accepted on 02-Nov-2017

Automatic analysis of facial actions: a survey

As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face