face recognition attendance system source code in java

For example, if a single face is detected at the previous 300ms, but before the next 300ms the face is tilts to the right, we need to wait for the next 300ms before our code can detect the change. In the above code, we set the frame rate to 300ms, but this can cause a minor problem with lag. In the above code, we used setInterval to define our frame rate per second at which we want to obtain a frame from the video and run the facial recognition code.

If you’ll recall, this was a point I made and said we’d return to-it’s time to address this issue. And then a cnn model is built same as that used for image classification, just that this time around it is a regression problem, which aim is to predict the facial features point.Īnd since the video needs to be processed in real-time, we need to define our own frame rate to consistently obtain frames from the video.

Now we are set to create a model (let assume we are using YOLO) to predict the exact same thing in the train/label/image1.txt if train/image1.jpg is passed as input into the model.įacial LandMark: Different coordinate point in which some facial features (such as eye, nose, mouth ) are located.

The train/label/image1.txt contains the boundary box for the location of the face coordinate in train/image1.jpg, the label file contains numbers in this format label x y width height 0, 0.18, 0.19, 1.95, 1.08 Train/image1.jpg train/label/image1.txt train/imag2.jpg train/label/image2.txt

Now we have a dataset containing the face and a corresponding file containg the boundary box of the face.

Or better still, we can download some set of already annotated face dataset, you can check here for examples of such dataset.

Lot of tools such as LabelImg or VOTT can be used to annotate the image.

First you can decide to annotate the face your self, what I mean by annotate is to draw a rectangular box over the regoin where the face is located.

Just that this time around our object is the face and not a car.

Predict the bounding box for a detected object, and compare the predicted box with the original bounding box of the input imageįace detection: Same principles in object detection is also applied for detecting Face in an image.Pass an input image through a deep learning model, such as YOLO.Leverages training data containing images with bounding boxes framing the target object-see the right-hand side of the image above (the blue boxes).There are different deep learning algorithms for object detection, such as YOLO, SSD, and the like, but since this tutorial is not explicitly focusing on this task, what object detection basically does is:

Face recognition attendance system source code in java