Release the resources that weren’t deallocated(if any). Save the image to our current working directory using the imwrite() method. Matches = fr.compare_faces(known_name_encodings, face_encoding)įace_distances = fr.face_distance(known_name_encodings, face_encoding)Ĭv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2)Ĭv2.rectangle(image, (left, bottom - 15), (right, bottom), (0, 0, 255), cv2.FILLED)Ĭv2.putText(image, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)ĭisplay the image using the imshow() method of the cv2 module. for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): Now, draw a rectangle with the face location coordinates using the methods from the cv2 module. Now, we pick the minimum valued distance from it indicating that this face of the test image is one of the persons from the training dataset. Then calculate the facial distance meaning that we calculate the similarity between the encoding of the test image and that of the train images. Then we compare this encoding with the encodings of the faces from the “train” dataset. We loop through each of the face locations and its encoding found in the image. face_locations = fr.face_locations(image)įace_encodings = fr.face_encodings(image, face_locations) Using those location values we can easily find the face encodings. The face_recognition library provides a useful method called face_locations() which locates the coordinates (left, bottom, right, top) of every face detected in the image. Read the test image using the cv2 imread() method. Test the model on the test datasetĪs mentioned above, our test dataset only contains 1 image with all of the persons in it. Known_names.append(os.path.splitext(os.path.basename(image_path)).capitalize()) 3. We loop through each of the images in our train directory, extract the name of the person in the image, calculate its face encoding vector and store the information in the respective lists. path = "./train/"įace encoding is a vector of values representing the important measurements between distinguishing features of a face like the distance between the eyes, the width of the forehead, etc. Now, create 2 lists that store the names of the images (persons) and their respective face encodings. The face_recognition library contains the implementation of the various utilities that help in the process of face recognition. Train the modelįirst import the necessary modules. Make sure that the images you’ve selected show the features of the face well enough for the classifier.įor testing the model, let’s take a picture containing all of the cast and place it onto our “test” directory.įor your comfort, we have added training and testing data with the project code. Pick an image for each of the cast from the internet and download it onto our “train” directory. Prepare the datasetĬreate 2 directories, train and test. It contains the implementation of various algorithms and deep neural networks used for computer vision tasks. OpenCV is an open-source library written in C++. Face recognition involves 3 steps: face detection, feature extraction, face recognition. The facial picture has already been removed, cropped, scaled, and converted to grayscale in most cases. Steps to develop face recognition modelīefore moving on, let’s know what face recognition and detection are.įace recognition is the process of identifying or verifying a person’s face from photos and video frames.įace detection is defined as the process of locating and extracting faces (location and size) in an image for use by a face detection algorithm.įace recognition method is used to locate features in the image that are uniquely specified. The dataset is included with face recognition project code, which you downloaded in the previous section. For this project, let’s take the cast of the popular American web series “Friends” as the dataset. We can do this face recognition project using our own dataset. Please download the source code of python face recognition project: Face Recognition Project Code Project Dataset Now, install face_recognition module using the below command pip install face_recognition Download Face Recognition Python Code To install the face_recognition, install the dlib package first. To install the above packages, use the following command. Join DataFlair on Telegram!! Tools and Libraries Stay updated with latest technology trends
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