![]() Num_components = 5 # Number of principal components Y = np. A randomly selected subset of 500 images are loaded, all the following experiments will be done on these images. The dataset consists of a bunch of images of people’s faces taken from MIT Faces Recognition Project database. The following figure shows the basic algorithm to compute a PCA, the interactive visual demo of which appears here. This is an application of principal components analysis (PCA) and k-means to the analysis of “ familiar faces.” We’ll also create a simple interactive visualization for exploring this dataset (using bokeh). ![]() The following description of the problem is taken straightaway from the assignment. This problem appeared as an assignment in the edX course Analytics for Computing (by Georgia Tech). In this article, a few problems will be discussed that are related to face reconstruction and rudimentary face detection using eigenfaces (we are not going to discuss about more sophisticated face detection algorithms such as Voila-Jones or DeepFace).
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