Face Detection by Literature

Please ping me if you know something more.

Multi-view Face Detection Using Deep Convolutional Neural Network

  1. Train face classifier with face (> 0.5 overlap) and background (<0.5 overlap) images.
  2.  Compute heatmap over test image scaled to different sizes with sliding window
  3.  Apply NMS .
  4.  Computation intensive, especially for CPU.
  •  http://arxiv.org/abs/1502.02766



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

Keywords: object proposals, facial parts,  more annotation.

  1. Use facial part annotations
  2. Bottom up to detect face from facial parts.
  3. “Faceness-Net’s pipeline consists of three stages,i.e. generating partness maps, ranking candidate windows by faceness scores, and refining face proposals for face detection.”
  4. Train part based classifiers based on attributes related to different parts of the face i.e. for hair part train ImageNet pre-trained network for color classification.
  5. Very robust to occlusion and background clutter.
  6. To much annotation effort.
  7. Still object proposals (DL community should skip proposal approach. It complicate the problem by creating a new domain of problem :)) ).
  • http://arxiv.org/abs/1509.06451



Supervised Transformer Network for Efficient Face Detection

  • http://home.ustc.edu.cn/~chendong/STN_Detector/stn_detector.pdf


UnitBox: An Advanced Object Detection Network

  • http://arxiv.org/abs/1608.02236


Deep Convolutional Network Cascade for Facial Point Detection

  • http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sun_Deep_Convolutional_Network_2013_CVPR_paper.pdf
  • http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm
  • https://github.com/luoyetx/deep-landmark


WIDER FACE: A Face Detection Benchmark

A novel cascade detection method being a state of art at WIDER FACE

  1. Train separate CNNs for small range of scales.
  2. Each detector has two stages; Region Proposal Network + Detection Network
  • http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/
  • http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/paper.pdf


DenseBox (DenseBox: Unifying Landmark Localization with End to End Object Detection)

Keywords: upsampling, hardmining, no object proposal, BAIDU

  1.  Similar to YOLO .
  2.  Image pyramid of input
  3.  Feed to network
  4. Upsample feature maps after a layer.
  5. Predict classification score and bbox location per pixel on upsampled feature map.
  6. NMS to bbox locations.
  7. SoA at MALF face dataset
  • http://arxiv.org/pdf/1509.04874v3.pdf
  • http://www.cbsr.ia.ac.cn/faceevaluation/results.html

Face Detection without Bells and Whistles

Keywords: no NN, DPM, Channel Features

  1. ECCV 2014
  2. Very high quality detections
  3. Very slow on CPU and acceptable on GPU
  • https://bitbucket.org/rodrigob/doppia/
  • http://rodrigob.github.io/documents/2014_eccv_face_detection_with_supplementary_material.pdf