I've partnered with OpenCV.org to bring you official courses in. Contribute to opencv/opencv development by creating an account on GitHub. Also, If you can use a GPU, then MMOD face detector is the best option as it is very fast on GPU and also provides detection at various angles. As we discussed earlier, I think this is the major drawback of Dlib based methods. 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. The images were annotated by its author. It would be safe to say that it is time to bid farewell to Haar-based face detector and DNN based Face Detector should be the preferred choice in OpenCV. Just initialize the model using cv2.CascadeClassifier followed by detection using cv2.detectMultiScle. So, we evaluate the methods on CPU only and also report result for MMOD on GPU as well as CPU. Processor : Intel Core i7 6850K – 6 Core RAM : 32 GB GPU : NVIDIA GTX 1080 Ti with 11 GB RAM OS : Linux 16.04 LTS Programming Language : Python. Moreover, Dlib provides a more advanced CNN based face detector, however, that does not work in real-time on a CPU which is one of the goals we are looking for so it has been ignored in this article. Contribute to opencv/opencv development by creating an account on GitHub. Also note the difference in the way we read the networks for Caffe and Tensorflow. So they introduced a Cascade of Classifiers, where the features are grouped. Given below are the Precision scores for the 4 methods. Does not work for side face and extreme non-frontal faces, like looking down or up. The second reason is that dlib is unable to detect small faces which further drags down the numbers. Haar cascades as expected performed the worst out of all of them having a lot of false positives as well. Example: These images from Unsplash were very large so I decided to check out some small images as well to see the performance on them. The concept involved here to identify the mouth region using dlib, measure the distance between the corners of the lips when the user smiles and immediately capture a picture. Also if the size of images is very extreme and there is a surety that lighting will be good along with minimum occlusion and mainly front-facing faces MTCNN might give the best results as seen when we were comparing the images. There is also a quantized Tensorflow version that can be used but we will use the Caffe Model. On closer inspection I found that this evaluation is not fair for Dlib. Although it is written in C++ it has python bindings to run it in python. I am an aspiring data scientist with a passion for teaching. Nonetheless, if you want to read about it you can refer here. In the above code, the image is converted to a blob and passed through the network using the forward() function. On the other hand, OpenCV-DNN method can be used for these since it detects small faces. So the results have considerably improved by taking full-size images, however, this way the DNN module has not been able to make any prediction where the face size is large. On closer inspection, we can see that it did not perform well on images having small face sizes that might have occurred due to resizing it to 300x300 before starting so let’s see how it would have performed if the original size was taken. Load the MTCNN module from mtcnn.mtcnn and initialize it. We notice that the OpenCV DNN detects all the faces while Dlib detects only those faces which are bigger in size. The model can be downloaded from the dlib-models repository. So considering the above two points, MTCNN would be the best bet if we were to deal with extreme face sizes and can be said to be leading the competition till now. The values reported are obtained using an Intel i5 7th gen processor and the image size passed is 640x360 except for the DNN module which is passed a 300x300 image as it has been done until now. All the captured images are stored in the same folder as your project. You will also receive a free Computer Vision Resource Guide. OpenCV’s DNN module hit a home run here. All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated. One of the captured pictures is given below. Before I start, I would like to give picture credits to Bruce Dixon, Chris Curry, Chris Murray, Ethan Johnson, Jerry Zhang, Jessica Wilson, Roland Samuel, and Tim Mossholder whose images I have used. To establish the ration of the mouth we need to find the distance between the corner of the lips, top and bottom of the lip and the left and right regions of the mouth. Where, AP_50 = Precision when overlap between Ground Truth and predicted bounding box is at least 50% ( IoU = 50% ) AP_75 = Precision when overlap between Ground Truth and predicted bounding box is at least 75% ( IoU = 75% ) AP_Small = Average Precision for small size faces ( Average of IoU = 50% to 95% ) AP_medium = Average Precision for medium size faces ( Average of IoU = 50% to 95% ) AP_Large = Average Precision for large size faces ( Average of IoU = 50% to 95% ) mAP = Average precision across different IoU ( Average of IoU = 50% to 95% ). We recommend to use OpenCV-DNN in most. We will see an example where, in the same video, the person goes back n forth, thus making the face smaller and bigger. You can however, train your own face detector for smaller sized faces. Read More…. The model comes embedded in the header file itself. The real surprise was MTCNN. Generally, we don’t work with such 3000x3000 images so it should not be a problem. My goal is to use AI in the field of education to make learning meaningful for everyone. script used for evaluating the OpenCV-DNN model, Stanford MRNet Challenge: Classifying Knee MRIs, Experiment Logging with TensorBoard and wandb, Deep Learning based Face Detector in OpenCV, Deep Learning based Face Detector in Dlib. ... Stump-based 20x20 gentle adaboost frontal face detector. To do this just change (300, 300) in cv2.dnn.blobFromImage() to original width and height respectively and remove the resize function. Dlib is a C++ toolkit containing machine learning algorithms used to solve real-world problems. In the above code, we have used the dawContours to draw a red coloured box. I am an entrepreneur with a love for Computer Vision and Machine Learning with a dozen years of experience (and a Ph.D.) in the field. The first step is to identify the region around the mouth. Dlib and MTCNN had pretty even performance with one edging the others and visa-versa. Created by Rainer Lienhart. 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. Next, these candidates are passed to another CNN which rejects a large number of false positives and performs calibration of bounding boxes. It is a fun and easy implementation using OpenCV and dlib. Throughout the post, we will assume image size of 300×300. Moreover, it also gave the quickest fps among all. Recently, it has been quite a lot in the news due to racial profiling incidents as elaborated here and here where people of color are being misidentified more than white people. This returns a JSON style dictionary which has the coordinates of the faces along with their confidence of prediction and the coordinates of facial landmarks detected. So, if you know that your application will not be dealing with very small sized faces ( for example a selfie app ), then HoG based Face detector is a better option. Once this is done we just need to auto-capture the image. in 2016 in their paper, “Joint Face Detection and Alignment Using Multi-task Cascaded Convolutional Networks.” It not only detects the face but also detects five key points as well.
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