A survey on deep learning based face recognition

This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on different scenarios. In particular, we evaluate the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face recognition, as well as active authentication on mobile devices. In addition, we show the lacks of using common lightweight models unchanged for specific face recognition tasks, by assessing the performance of the original lightweight versions of the lightweight face models considered in our study. We also show that the inference time on different devices and the computational requirements of the lightweight architectures allows their use on real-time applications or computationally limited platforms. In summary, this paper can serve as a baseline in order to select lightweight face architectures depending on the practical application at hand. Besides, it provides some insights about the remaining challenges and possible future research topics.

Benchmarking lightweight face architectures on specific face recognition scenarios

The field of biometrics research encompasses the need to associate an identity to an individual based on the persons physiological or behaviour traits. While the use of intrusive techniques such as retina scans and finger print identification has resulted in highly accurate systems, the scalability of such systems in real-world applications such as surveillance and border security has been limited. As a branch of biometrics research, the origin of soft biometrics could be traced back to need for non-intrusive solutions for extracting physiological traits of a person. Following high number of research outcomes reported in the literature on soft biometrics, this paper aims to consolidate the scope of soft biometrics research across four thematic schemes (i) a detailed review of soft biometrics research data sets, their annotation strategies and building a largest novel collection of soft traits; (ii) the assessment of metrics that affect the performance of soft biometrics system; (iii) a comparative analysis on feature and modality level fusion reported in the literature for enhancing the system performance; and (iv) a performance analysis of hybrid soft biometrics recognition system using multi-scale criterion. The paper also presents a detailed analysis on the global traits associated to person identity such as gender, age and ethnicity. The contribution of the paper is to provide a comprehensive review of scientific literature, identify open challenges and offer insights on new research directions in the filed.

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Soft biometrics: a survey

In this paper, we analyze some of our real-world deployment of face recognition (FR) systems for various applications and discuss the gaps between expectations of the user and what the system can deliver. We evaluate some of the existing algorithms with modifications for applications including FR on wearable devices (like Google Glass) for improving social interactions, monitoring of elderly people in senior citizens centers, FR of children in child care centers and face matching between a scanned IC/passport face image and few live webcam images for automatic hotel/resort checkout or clearance. Each of these applications poses unique challenges and demands specific research components so as to adapt in the actual sites.

Face recognition: Perspectives from the real world

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Small face detection using deep learning on surveillance videos

Face recognition (FR) is a natural and intuitive way for human beings to identify or verify or at least get familiar and interact with other members of the community. Hence, human beings expect and endeavor to develop similar competency in machine recognition of human faces. Due to the rapid increase in computing power in recent decades and the need to automate the FR tasks for many applications, researchers from diverse areas like cognitive and computer sciences are making efforts in understanding how humans and machines recognize human faces respectively. Its application is innumerable (like access control, surveillance, social interactions, e-commerce, just to name a few). In this chapter we will review two aspects of FR: machine recognition of faces and how human beings recognize human faces. We will also discuss the recent benchmark studies, their protocols and databases for FR and psychophysical studies of FR abilities of human beings.

Trends in Machine and Human Face Recognition

In the light of the human studies that report a strong correlation between head circumference and body size, we propose a new research problem: head-body matching. Given an image of a person's head, we want to match it with his body (headless) image. We propose a dual-pathway framework which computes head and body discriminating features independently, and learns the correlation between such features. We introduce a comprehensive evaluation of our proposed framework for this problem using different features including anthropometric features and deep-CNN features, different experimental setting such as head-body scale variations, and different body parts. We demonstrate the usefulness of our framework with two novel applications: head/body recognition, and T-shirt sizing from a head image. Our evaluations for head/body recognition application on the challenging large scale PIPA dataset (contains high variations of pose, viewpoint, and occlusion) show up to 53% of performance improvement using deep-CNN features, over the global model features in which head and body features are not separated or correlated. For T-shirt sizing application, we use anthropometric features for head-body matching. We achieve promising experimental results on small and challenging datasets.

Mona Ragab Sayed

Papers

Trends in Machine and Human Face Recognition

Which Body Is Mine

Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance.