The frontal face detector provided by dlib works using features extracted by Histogram of Oriented Gradients (HOG) which are then passed through an SVM. For more detailed reading, you can refer here.
I tried to evaluate the 4 models using the FDDB dataset using the script used for evaluating the OpenCV-DNN model. I created two small databases containing 10 images each with one created from photos from Unsplash and the other from Google to see how the techniques fare on both large and small images. How I Created The Credit Card Reader Using OpenCV? It failed to detect the face in even a single frame suggesting lighting conditions need to good if it is to be used. One of the captured pictures is given below.
When the light was switched on the DNN module was the back at its work providing completely accurate predictions. (For face recognition task another splits should be created) Unpack dataset file to some folder and place split files into the same folder. As you can see it is very easy to make predictions using Haar cascades. OpenCV-Python; Haar Cascades Data File; i3 or higher core processor (CPU)/ 2.1 GHz or higher; Photo images for testing; I used a 2010 Sony VAIO laptop with an i3 processor 2.1 GHz with 8 GB of memory running Windows 7 Professional with at minimum Service Pack 1 installed. Please download the code from the link below. We will see an example where, in the same video, the person goes back n forth, thus making the face smaller and bigger. In most applications, we won’t know the size of the face in the image before-hand. Open Source Computer Vision Library. The others were no match for it and failed at large angles and quick movement. Dlib and MTCNN had pretty even performance with one edging the others and visa-versa. We could not see any major drawback for this method except that it is slower than the Dlib HoG based Face Detector discussed next. Copyright Analytics India Magazine Pvt Ltd, Complete Guide On NLP Profiler: Python Tool For Profiling of Textual Dataset, Extraction Of Aadhar IDs Using OpenCV & TensorFlow- Sushil Ostwal, Head Data Science at Motilal Oswal Financial Services, Do Developers Need Theoretical Knowledge For AI Programming? The output coordinates of the bounding box are normalized between [0,1]. adaboost frontal face detector. Read More…. The model can be downloaded from the dlib-models repository. My goal is to use AI in the field of education to make learning meaningful for everyone. Non-frontal can be looking towards right, left, up, down. Note: Dlib’s prediction sometimes misses the chin or the forehead due to the face that is was manually annotated by Davis King, the author of Dlib, so if the task you are working on cannot afford this don’t use Dlib. Take a look, classifier = cv2.CascadeClassifier('models/haarcascade_frontalface2.xml'), detector = dlib.get_frontal_face_detector(), modelFile = "models/res10_300x300_ssd_iter_140000.caffemodel", Rapid Object Detection using a Boosted Cascade of Simple Features, Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks, https://www.linkedin.com/in/vardan-agarwal-6bb123168/, How to do visualization using python from scratch, 5 Types of Machine Learning Algorithms You Need to Know, 5 YouTubers Data Scientists And ML Engineers Should Subscribe To, 5 Neural network architectures you must know for Computer Vision, 21 amazing Youtube channels for you to learn AI, Machine Learning, and Data Science for free, Comparison results on videos along with the frame rate achieved. If a window fails at the first stage, these remaining features in that cascade are not processed.
This is mainly because the CNN features are much more robust than HoG or Haar features. DNN module had an all or nothing type of performance. If you liked this article and would like to download code (C++ and Python) and example images used in this post, please subscribe to our newsletter. The 68 point landmark detector is part of the dlib library. CIO Virtual Round Table Discussion On Data Integrity | 26th Nov |, Full-Day Workshop: Build your own GANs from Scratch | 28th November |, Webinar On Data Science In The Post-COVID World | 28th November |. The model comes embedded in the header file itself. We can get rid of this problem by upscaling the image, but then the speed advantage of dlib as compared to OpenCV-DNN goes away.
Also note the difference in the way we read the networks for Caffe and Tensorflow. They were proposed way back in 2001 by Paul Viola and Micheal Jones in their paper, “Rapid Object Detection using a Boosted Cascade of Simple Features.” It is super fast to work with and like the simple CNN, it extracts a lot of features from images. DNN came a close second and was not able to identify 3 faces. We recommend to use OpenCV-DNN in most. It uses a dataset manually labeled by its Author, Davis King, consisting of images from various datasets like ImageNet, PASCAL VOC, VGG, WIDER, Face Scrub. So before moving on to the video part let’s recap the things we have learned from the results of this section. There is a lot of discrepancy around the value for green. Before we implement a smile detector we need to recognize the face. Given below are some examples. The first step is to identify the region around the mouth. OpenCV provides 2 models for this face detector.
We run each method 10000 times on the given image and take 10 such iterations and average the time taken.
Otherwise, we use the quantized tensorflow model. Have any other suggestions? We share some tips to get started. Thus, it is better to use OpenCV – DNN method as it is pretty fast and very accurate, even for small sized faces. The goal here was to see how well these models perform in very low light and when a light source is right behind the person. It is a fun and easy implementation using OpenCV and dlib. Dlib HoG is the fastest method on CPU. This reduces the original 160000+ features to 6000 features.
In the HOG feature descriptor, the distribution of the directions of gradients is used as features.
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