How to Recognize an Actress without her Makeup

By looking at the title you would have thought that you are at the wrong place in a technical blog. Well, technology is nothing if you don't find a proper implementation of it. In this blog post, we are looking at how to utilize image processing to identify the actress when they not under makeup. 

Thanks to this link, it was possible to find 26 Bollywood actresses with and without makeup. 

Then those two images were classified into two folders with and without makeup. 

Here are those actresses with their fancy makeup. 

Then we have another set of images where these beautiful actresses without makeup as shown below.

Now our task is to see whether we can match the non-makeup actresses with images of when they are under makeup. Important to note that when they not under makeup, image resolutions are very low which is understandable. So we need to consider that as an environmental condition without complaining about it. 

We used the Orange Data Mining tool previously for different image processing applications which you can get from this link 

In Image Embedding, the openface embedded is used as it is the most common embedded that can be used to detect faces. 

After the embedding is completed, images will be vectorized into different parameters that can be viewed from a Data Table as shown below. 

Then similar embedding will be carried out to the non-makeup images and connected by Neighbours control to find out who are the closest images. In the Neighbours Eclidiean, Manhattan, Mahalanobis, Cosine, Jaccard Spearman, Absolute Spearman, Pearson, Absolute Pearson distance measures can be used to determine the better results. 

When we clicked an image from the non-makeup list, closet images will be displayed at the Image viewer at the Neighbours control. 

Since we have asked to display three closest images, for the Actress Nargiz, it has first match the Nargix with makeup and two other images. 

Similarly, the Kajal image also matched as shown below. 

Out of the distance measures, Pearson distance was able to identify 9 out of 26 actresses and nearly match another 12 actresses. On the other hand, the Spearman distance measure identified 8 actresses while it nearly matches another 14 actresses. The Pearson measure could not identify five actresses while the Spearman distance measure could not identify three actresses. 

Of course, this dataset is not a rich dataset. So there is major room for improvement with the proper dataset. 

Finally, makeup will make you beautiful but can't hide your identity!!