Tribal marking is an African cultural practice which is carried out for the purpose of
identifying a person's tribe or family. In facial recognition systems, tribal marks are considered soft biometrics, which
have been used to improve the
performance of facial recognition systems. Facial mark recognition (FMR) refers to the
ability of a system to determine whether a small skin patch taken from a facial image
contains a facial mark. Although there have been significant improvements in detecting
facial marks using Convolutional Neural Networks, a system integrating African facial marks has
not been implemented yet. In this thesis, we implemented a facial recognition system
for African tribal marks using a one-shot learning model which employed a collected dataset consisting of
images of people with tribal marks. Due to limited sources of data, we adopted Data
Augmentation techniques to increase the size and balance of our dataset. Face detection
and extraction was carried out by a mtcnn model, after which embedding points were
created using a pre-trained (Facenet) model. After the points were created, we employed a
classifier which matched the faces to their appropriate classes based on the training dataset. We evaluated our model using various evaluation metrics, and we obtained an accuracy of 100% for training and 88% for testing for the first experiment and 99% and 83% for the second experiment. We evaluated the model's performance further using F1 score and MCC, and we reported a score of 0.887 and 0.757,
respectively, for the first experiment and 83% and 0.733 for the second experiment. This
study can prove useful in areas such as twin identification, profiling, forensic analysis,
etc.