🚀 😏 CPU (Near) Real Time face detection


How to install

$ pip install git+https://github.com/iitzco/faced.git

Soon to be available on PyPI.

How to use

As library

import cv2

from faced import FaceDetector
from faced.utils import annotate_image

face_detector = FaceDetector()

img = cv2.imread(img_path)
rgb_img = cv2.cvtColor(img.copy(), cv2.COLOR_BGR2RGB)

# Receives RGB numpy image (HxWxC) and
# returns (x_center, y_center, width, height, prob) tuples. 
bboxes = face_detector.predict(rgb_img, thresh)

# Use this utils function to annotate the image.
ann_img = annotate_image(img, bboxes)

# Show the image

As command-line program

# Detection on image saving the output
$ faced --input imgs/demo.png --save


# Live webcam detection
$ faced --input webcam


# Detection on video with low decision threshold
$ faced --input imgs/demo.mp4 --threshold 0.5

See faced --help for more information.




CPU (i5 2015 MBP) GPU (Nvidia TitanXP)
~5 FPS > 70 FPS

Comparison with Haar Cascades

Haar Cascades are one of the most used face detections models. Here's a comparison with OpenCV's implementation showing faced robustness.

faced Haar Cascade
demo_yolo demo_haar
foo-faced foo-haar
gino-faced gino-haar

About faced

faced is an ensemble of 2 deep neural networks (implemented using tensorflow) designed to run at Real Time speed in CPUs.

Stage 1:

A custom fully convolutional neural network (FCNN) implementation based on YOLO. Takes a 288x288 RGB image and outputs a 9x9 grid where each cell can predict bounding boxes and probability of one face.


Stage 2:

A custom standard CNN (Convolutions + Fully Connected layers) is used to take a face-containing rectangle and predict the face bounding box. This is a fine-tunning step. (outputs of Stage 1 model is not so accurate by itself, this is a corrector step that takes the each bouding box predicted from the previous step to improve bounding box quality.)


Why not just perform transfer learning on trained YOLO (or MobileNet+SSD) ?

Those models were designed to support multiclass detection (~80 classes). Because of this, these networks have to be powerfull enough to capture many different low and high level features that allow them to understand the patterns of very different classes. Powerful in this context means large amount of learnable parameters and hence big networks. These big networks cannot achieve real time performance on CPUs. [1]

This is an overkill for the simple task of just detecting faces. This work is a proof of concept that lighter networks can be designed to perform simpler tasks that do not require relatively large number of features.

[1] Those models cannot perform Real Time on CPU (YOLO at least). Even tiny-yolo version cannot achieve 1 fps on CPU (tested on 2015 MacBook Pro with 2.6 GHz Intel Core i5).

How was it trained?

Training was done with WIDER FACE dataset on Nvidia Titan XP GPU.

If you are interested in the training process and/or data preprocessing, just raise an issue and we'll discuss it there.

How to run on GPU?

Just install tensorflow-gpu instead of tensorflow.


🚧 Work in progress 🚧
Models will be improved and uploaded.

This is not a Production ready system. Use it at your own risk.