Abstract
Face segmentation represents an active area of research within the bio-metric community in particular and the computer vision community in general. Over the last two decades, methods for face segmentation have received increasing attention due to their diverse applications in several human-face image analysis tasks. Although many algorithms have been developed to address the problem, face segmentation is still a challenge not being completely solved, particularly for images taken in wild, unconstrained conditions. In this paper, we present a comprehensive review of face segmentation, focusing on methods for both the constrained and unconstrained environmental conditions. The article illustrates the advantages and disadvantages of previously proposed methods in state-of-the-art (SOA). The approaches presented comprise the seminal works on face segmentation and culminating in SOA approaches of the deep learning architecture. An extensive comparison of the previous approaches is intuitively presented, with a discussion of the potential directions for future research on the topic. We believe this comprehensive review and recap will contribute to a number of application domains, and will augment the knowledge of the research community.
Highlights
Image segmentation is of immense importance to mid-level computer vision tasks, that target the jointly grouping of image regions into coherent parts of objects
From an implementation point of view, it is the primary task in computer vision, which allows the computer to understand and see the image contents, classify a region or pixel in the image, and divide the image into different parts according to visual understanding
We present the advantages and disadvantages of SOA methods by initially focusing on the seminal approaches for face segmentation, culminating in SOA approaches based on the deep learning architecture
Summary
Image segmentation is of immense importance to mid-level computer vision tasks, that target the jointly grouping of image regions into coherent parts of objects. From an implementation point of view, it is the primary task in computer vision, which allows the computer to understand and see the image contents, classify a region or pixel in the image, and divide the image into different parts according to visual understanding. In this regard, extensive research work already exists on image segmentation, mainly reported in the Pascal Visuall Object Classes challenge [1]. Face segmentation is regarded as an essential and intermediary step for subsequent human face image analysis This includes applications from the fields of bio-metric based identification and recognition, human indexing, and robotic control, all the way to medical and mental-state understanding. Face segmentation plays a crucial role in the development of various intelligent environments
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