Challenges in Real-Life Face Recognition with Heavy Makeup and Occlusions on the New Benchmark Data Set Example

We study a Face Recognition problem caused by unforeseen in the training set variations in face images, related to artistic makeup and other occlusions. Existing Artificial Neural Networks (ANNs) have achieved a high recognition accuracy; however, in the presence of significant variations, they perform poorly. We introduce a new data set of face images with variable makeup, hairstyles and occlusions, named BookClub artistic makeup face data, and then examine the performance of the ANNs under different conditions. In our experiments, the recognition accuracy has decreased dramatically when the test images include an unseen type of makeup and occlusions, happened in a real-world scenario. We show that the fusion off the training set with several heavy makeups and other occlusion images can improve the performance. We show that the makeup and other occlusions can be used not only to disguise a person’s identity from the ANN algorithms but also to spoof a wrong identification.

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