From Facial Parts Responses to Face Detection: A Deep Learning Approach

Yang, Shuo; Luo, Ping; Loy, Chen Change; Tang, Xiaoou

doi:10.1109/iccv.2015.419

Cited by 496 publications

(381 citation statements)

References 32 publications

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“…Ranjan et al (2015) combine deep pyramidal features with Deformable Part Models. Recently, Yang et al (2015b) proposed a DCNN architecture that is able to discover facial parts responses from arbitrary uncropped facial images without any part supervision and report state-of-the-art performance on current face detection benchmarks.…”

Section: Face Detectionmentioning

confidence: 99%

“…Convolutional Neural Networks Another category, similar to the previous rigid template-based ones, includes the employment of Convolutional Neural Networks (CNNs) and Deep CNNs (DCNNs) (Osadchy et al 2007;Ranjan et al 2015;Li et al 2015a;Yang et al 2015b). Osadchy et al (2007) use a network with four convolution layers and one fully connected layer that rejects the non-face hypotheses and estimates the pose of the correct face hypothesis.…”

Section: Face Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Face Detectionmentioning

confidence: 99%

Section: Face Detectionmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Another important dimension is the type of classifier used: while various classifiers such as SVMs have been used, cascade classifiers have been popular due to their efficiency [1]- [3], [5], [17]. Finally, methods vary based on whether the computed features are aggregated over an entire region or a part-based analysis is performed; this set includes Deformable Part Model [17], Tree Parts Model (TSM) [12], structure model [14], Deep Pyramid Deformable Part Model [23], Faceness [18].…”

Section: Introductionmentioning

confidence: 99%

A multi-scale cascade fully convolutional network face detector

Yang

Nevatia

2016

2016 23rd International Conference on Pattern Recognition (ICPR)

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Abstract-Face detection is challenging as faces in images could be present at arbitrary locations and in different scales. We propose a three-stage cascade structure based on fully convolutional neural networks (FCNs). It first proposes the approximate locations where the faces may be, then aims to find the accurate location by zooming on to the faces. Each level of the FCN cascade is a multi-scale fully-convolutional network, which generates scores at different locations and in different scales. A score map is generated after each FCN stage. Probable regions of face are selected and fed to the next stage. The number of proposals is decreased after each level, and the areas of regions are decreased to more precisely fit the face. Compared to passing proposals directly between stages, passing probable regions can decrease the number of proposals and reduce the cases where first stage doesn't propose good bounding boxes. We show that by using FCN and score map, the FCN cascade face detector can achieve strong performance on public datasets.

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“…In order to solve two problems mentioned above, we adopt different methods to deal with face normalization, and the flow of this strategy is described in Figure 8. First of all, an image is detected by a face detector based on CNN [13]. And then, we estimate the pose and locate face landmarks of the detected face through 3D poses algorithm [14].…”

Section: Face Representationmentioning

confidence: 99%

Face Verification with Multi-Task and Multi-Scale Features Fusion

Lu¹,

Yang²,

Zhang³

et al. 2017

Preprint

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Abstract:Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

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From Facial Parts Responses to Face Detection: A Deep Learning Approach

Cited by 496 publications

References 32 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A multi-scale cascade fully convolutional network face detector

Face Verification with Multi-Task and Multi-Scale Features Fusion

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