Unfortunately it is not suitable for real time video.
Meanwhile, the CNN based detector is capable of detecting faces almost in all angles. Applying convolution of 3 X 3 on it will result in a 6 X 6 matrix. This means that the input will be an 8 X 8 matrix (instead of a 6 X 6 matrix). Hence, we do not focus too much on the corners since that can lead to information loss To overcome these issues, we can pad the image with an additional border, i.e., we add one pixel all around the edges. Pixels present in the corner of the image are used only a few number of times during convolution as compared to the central pixels. The human visual system applies edge detection. Filters are frequently applied to images for different purposes. The number of trainable parameters is significantly smaller and therefore allow CNN to use many filters to extract interesting features. Convolution neural networks apply small size filter to explore the images. For face detection, the areas of interested are all localized.
A convolutional neural networks (CNN or ConvNet) is a type of deep learning neural network, usually applied to analyzing visual imagery whether it's detecting cats, faces or trucks in an image Algorithm #4 CNN Classifier + Lucas-Kanade tracking for consistency through video frames (~50-200ms per frame) Using a combination of x-corner saddle detection and an ML DNN Classifier trained off of the previous algorithms on tiles of saddle points, we can find a triangle mesh for 'mostly' chessboard corners in realtime' (~20ms per 960x554 px frame). However, their use in grayscale images has not been considered due to their design difficulties. CNN perform well for locating corner features in binary images. Due to their powerful local processing capabilities, Cellular Nonlinear/Neural Networks (CNN) are commonly utilized in image processing applications such as image edge detection, image encoding and image hole filling. Corner detection represents one of the most important steps to identify features in images.
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