Finding Tiny Faces

By Peiyun Hu and Deva Ramanan at Carnegie Mellon University.

Introduction

We develop a face detector (Tiny Face Detector) that can find ~800 faces out of ~1000 reportedly present, by making use of novel characterization of scale, resolution, and context to find small objects. Can you confidently identify errors?

Tiny Face Detector was initially described in an arXiv tech report.

In this repo, we provide a MATLAB implementation of Tiny face detector, including both training and testing code. A demo script is also provided.

Citing us

If you find our work useful in your research, please consider citing:

@article{hu2016finding,
  title={Finding Tiny Faces},
  author={Hu, Peiyun and Ramanan, Deva},
  journal={arXiv preprint arXiv:1612.04402},
  year={2016}
}

Installation

Clone the repo recursively so you have my fork of MatConvNet.

git clone --recursive git@github.com:peiyunh/tiny.git

Compile MatConvNet by running following commands in MATLAB (see Installing - MatConvNet for more details):

>> cd matconvnet/;
>> addpath matlab/; 
>> vl_compilenn('enableImreadJpeg', true, 'enableGpu', true, 'cudaRoot', [cuda_dir],...
                'cudaMethod', 'nvcc', 'enableCudnn', true, 'cudnnRoot', [cudnn_dir]);
>> vl_testnn('gpu', true);  % vl_testnn('gpu', false) for cpu-only 

Compile our MEX function in MATLAB:

>> cd utils/;
>> compile_mex; 

Download WIDER FACE and unzip data and annotation files to data/widerface such that:

$ ls data/widerface
wider_face_test.mat   wider_face_train.mat    wider_face_val.mat
WIDER_test/           WIDER_train/            WIDER_val/

Demo

We provide a minimal demo tiny_face_detector.m that runs our detector on an single input image and output face detections:

function bboxes = tiny_face_detector(image_path, output_path, prob_thresh, nms_thresh, gpu_id)

Here is a command you can run to reproduce our detection results on the world's largest selfie:

>> bboxes = tiny_face_detector('data/demo/selfie.jpg', './selfie.png', 0.5, 0.1, 1)

Training

To train a ResNet101-based Tiny Face Detector, run following command in MATLAB:

>> hr_res101('train');           % which calls cnn_widerface.m

After training, run the following command to test on the validation set:

>> hr_res101('test');            % which calls cnn_widerface_test_AB.m 

Finally, run the following command to evaluate the trained models:

>> hr_res101('eval');            % which calls cnn_widerface_eval.m

Please refer to scripts/hr_res101.m for more details on how training/testing/evaluation is configured.

Clustering

We derive canonical bounding box shapes by K-medoids clustering (cluster_rects.m). For reproducibility, we provide our clustering results in data/widerface/RefBox_N25.mat. We also provide the version after template resolution analysis in data/widerface/RefBox_N25_scaled.mat (Fig. 8 in our paper).

Evaluation

We provide both our own version of evaluation script (cnn_widerface_eval.m) and official evaluation script (eval_tools/). Our implementation runs much faster and is easier to customize. However, our version produces slightly lower numbers comparing to the official one. We use our evaluation script only for prototyping. All numbers in the paper are based on the official evaluation script.