A survey on face detection in the wild: Past, present and future

By Stefanos P. Zafeiriou, Cha Zhang, Zhengyou Zhang. 2015

483 citations recorded by the source corpus.

We present a comprehensive and survey for face detection 'in-the-wild'.We critically describe the advances in the three main families of algorithms.We comment on the performance of the state-of-the-art in the current benchmarks.We outline future research avenues on the topic and beyond. Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called 'in-the-wild') through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure (2019, background): “Even the seemingly simple task of facial recognition (Zafeiriou, Zhang, and Zhang 2015) has been shown to be subject to extreme amounts of algorithmic bias among select demographics (Buolamwini and Gebru 2018).”
Deep Learning for Generic Object Detection: A Survey (2018, unknown): “A Survey on Face Detection in the Wild: Past, Present and Future [301] 2015 CVIU 2000 年以降の “in the wild”(非制約環境,実環境)顔検出のサーベイ”
Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment (2016, background): “We denote this initial estimate as x, which for face alignment is commonly a mean face aligned to the output of a face detector [60].”
Detecting Masked Faces in the Wild with LLE-CNNs (2017, background): “As stated in [33], existing face detection models can be roughly grouped into three categories, in which models are based on boosting, Deformable Part Model (DPM) [6] and Convolutional Neural Network (CNN) [16], respectively.”
Facial Expression Analysis under Partial Occlusion (2018, methodology): “ A survey of face detection methods in a wild environment [Zafeiriou et al. 2015].”
Finding Tiny Faces in the Wild with Generative Adversarial Network (2018, background): “A thorough survey on face detection methods can be found in [32].”
Mitigating Bias in Gender, Age and Ethnicity Classification: A Multi-task Convolution Neural Network Approach (2018, background): “Automated FA is instrumental in a wide range of applications including face detection [2], soft biometrics / face attribute classification [3] and face recognition [4].”
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification (2018, background): “Automated facial image analysis describes a range of face perception tasks including, but not limited to, face detection (Zafeiriou et al., 2015; Mathias et al., 2014; Bai and Ghanem, 2017), face classiﬁcation (Reid et al., 2013; Levi and Hassner, 2015a; Rothe et al., 2016) and face recognition…”
3D Face Morphable Models "In-the-Wild" (2017, background): “During the past few years, we have witnessed significant improvements in various face analysis tasks such as face detection [19, 42] and 2D facial landmark localisation on static images [40, 21, 6, 38, 43, 4, 5, 36].”
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications (2018, background): “For a good overview of face detection in the wild see [VJ04,ZZZ15].”
Imbalance Problems in Object Detection: A Review (2019, unknown): “pedestrian detection, vehicle detection, face detection) [34], [35], [36], [37].”