Techniques and Approaches of Facial Recognition under Occlusion: A Review

A human face is one of the most prominent features used in the process of authenticating technical applications in the domains of security, biometrics, surveillance and forensics. Recognition and detection of facial features has thus become challenging due to problems of occlusion, emotion, image resolution, varying facial expressions and aging. Such attributes tend to have a great impact on the overall performance of a robust facial recognition system. Hence, facial recognition with presence of occlusion triggers to be a hindrance in the natural environment and thereby limits the system model to recognise faces. For this purpose, multiple research authors have inhibited strategies and techniques to address the issues of occlusion. Numerous developments in the field of machine learning and deep learning have constantly evolved with complex architectures that could design the model from scratch and perform image processing to attain maximum efficiency. Such approaches have the potential to accomplish highest state-of-the art accuracy by minimizing error loss. Nevertheless, facial recognition that tends to bypass occlusion is still imperative to limitations for real?world applications. Hence in this review paper, the authors highlight various problems that a facial recognition system with occlusion might face and thereby proposes to analyse various methods of recognition in order to cope with the existing problems. The paper also focuses on extraction approaches thus used present the novelty. The review finally ends, with a mention of future challenges with regards to occluded facial recognition

Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network

Modifying the facial images with desired attributes is important, though challenging tasks in computer vision, where it aims to modify single or multiple attributes of the face image. Some of the existing methods are either based on attribute independent approaches where the modification is done in the latent representation or attribute dependent approaches. The attribute independent methods are limited in performance as they require the desired paired data for changing the desired attributes. Secondly, the attribute independent constraint may result in the loss of information and, hence, fail in generating the required attributes in the face image. In contrast, the attribute dependent approaches are effective as these approaches are capable of modifying the required features along with preserving the information in the given image. However, attribute dependent approaches are sensitive and require a careful model design in generating high-quality results. To address this problem, we propose an attribute dependent face modification approach. The proposed approach is based on two generators and two discriminators that utilize the binary as well as the real representation of the attributes and, in return, generate high-quality attribute modification results. Experiments on the CelebA dataset show that our method effectively performs the multiple attribute editing with preserving other facial details intactly

Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.Comment: The first two authors have equal contributions. Corresponding author: Shifeng Zhang ([email protected]