## 2 Related Work
### Existing Methods  
The challenge of determining a person's ethnicity based solely on their visual traits was analyzed in [4]. Three primary ethnic groups were represented in this work: Mongolians, Caucasians, and blacks. The authors used 447 photos from the FERET database, 357 of which were used for training and 90 of which were used for testing. In order to solve the classification challenge, the authors extracted several geometric features and color attributes from the image. A CNN yielded a model with a precision of 98.6%, while an artificial neural network yielded a precision of just 82.4%. A new indexing strategy based on a hash table that uses a hierarchy of classifiers to predict attributes such as age, gender, and ethnicity was shown in [16]. The matching procedure selected only a tiny fraction of the database from the indexed hash table, lowering retrieval time while retaining low computational complexity. Transfer learning was used to train the hierarchical classifiers using a pre-trained CNN. A new probabilistic back-tracking approach that corrects misclassifications using conditional probabilities was proposed to decrease the classification error generated by classifiers. Based on expected qualities, a method called dynamic thresholding was proposed that dynamically sets the threshold for matching computations based on the results of these computations. It was necessary to perform extensive testing on classifiers to compare their results with those of the most recent approaches. On a large-scale database, the suggested indexing strategy showed a significant reduction in search time and a significant boost in accuracy over current face-image retrieval techniques. Probabilistic back-tracking and dynamic thresholding were shown to be important in statistical tests. Researchers have recently focused their attention on the human face and its traits, making it one of the most popular topics. Known as \"soft biometrics\", the features and information extracted from a person have been applied in various fields, including law enforcement, surveillance videos, advertising, and social media profiling, to improve recognition performance and the search engine for facial images. The authors of [6] discovered that there was no mention of the Arab world in relevant papers or Arab datasets. To identify these labels using deep-learning methodologies, the authors set out to generate an Arab dataset and properly label Arab sub-ethnic groups. Images from the Gulf Cooperation Council (GCC) countries, the Levant, and Egypt comprised the Arab image dataset that was developed. The challenge was solved by combining two different methods of learning. A CNN pre-trained model was utilized to obtain state-of-the-art outcomes in computer vision classification issues with supervised deep learning. In the second category of deep learning, there was unsupervised learning (deep clustering). Ethnic classification is one of the primary goals of employing unsupervised learning. To the best of this author's knowledge, that was the first time that deep clustering had been applied to ethnicity classification problems. Three approaches were considered for this. With the Arab dataset labels adjusted, the best results were 56.97% and 52.12% when pre-trained CNNs were evaluated on different datasets. Deep-clustering algorithms were applied to several datasets, and the accuracy (ACC) ranged from 32% to 59%, with Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI) values ranging from 0 to 0.2714 and 0.2543, respectively. The assessment of age, gender, and ethnicity in human face photographs is a crucial step in a variety of fields, including access control, forensics, and surveillance. Face recognition and facial aging can be better understood using demographic estimations. When performing such a study, demographics and face recognition and retrieval are two separate components. The initial step in [17] was to extract demographically informative features based on facial asymmetry to predict an image's age group, gender, and race.