## 2 Related Work
### Existing Methods
While face recognition focuses on recognizing and verifying the identity of a person from their facial features, ethnicity recognition aims to classify individuals based on their ethnic background or race. In the context of this study, CNN models have been utilized to extract features from facial images, which were then used to classify individuals into different ethnicity categories. Therefore, the techniques and methodologies used in face recognition, such as CNNs, can be applied to ethnicity recognition tasks. In the recent decade, the number of people interested in face recognition studies has increased significantly. Identifying people's ethnicities is one of the most difficult challenges in face recognition. New CNNs were used to construct a new model that can identify people's ethnicity based on their facial traits. Three different nationalities were represented by 3141 photos in the new dataset for identifying people based on ethnicity [20]. As far as this author knows, this is the first time that an image dataset for ethnicity had been collected and made available to the public. Each CNN was compared with two other models that were currently considered the best in the field: Inception V3 and VGG. Results demonstrated that the authors' model performed the best, with a verification accuracy of 96.9% from a set of photos of people that were evaluated. Unconstrained real-world facial photos are classified into preset ages and genders using age and gender forecasts of unfiltered faces. The standard methods, however, fail miserably when used with unfiltered benchmarks because of the wide range of changes in the unconstrained photos. Due to their superior performance in facial analysis, CNN-based approaches have recently been widely used for categorization. A novel end-to-end CNN approach was proposed in the work of [21] to accomplish robust age group and gender classification for unfiltered real-world faces. In the two-level CNN architecture, features were extracted and classified. Classification and feature extraction worked together to classify photos according to their age and gender based on the extracted features. A robust picture preparation approach was used to handle the huge variances in unfiltered real-world faces before they were input into the CNN model. It was technically possible to train the network on an IMDb-WIKI with noisy labels, then fine-tune it on MORPH-II and ultimately on the original OIU-Audience dataset. Looking at the OIU-Audience benchmark, the experimental results reveal that their model performs the best in age and gender classification. It showed improvement above the best-reported results by 16.6% (precise accuracy) and 3.2% (one-off accuracy) for age-group classification and gender classification, respectively. In computer vision, the study of human facial image analysis is a hot topic. A methodology for facial image analysis was proposed to address the three tough challenges of race, age, and gender detection through face parsing [22]. The authors used deep CNNs to train an end-to-end face parsing model by manually labeling face photos. A facial image was segmented into seven dense classes using a deep-learning-based segmentation technique. Using probabilistic classification, the authors constructed probability maps for each face category. Probability maps were employed as a means of characterizing features. They created a CNN model for each demographic job by extracting features from probability maps (race, age, and gender). Extensive studies on state-of-the-art datasets yielded significantly better results than those previously obtained. With the growth of the digital age and the rise of human-computer connections, the desire for gender classification systems has increased. Automated classification of gender could be used for various purposes, including indexing facial photos based on gender, monitoring gender-restricted places, gender-adaptive targeted marketing, and collecting passive data on gender demographics [23].