## 2 Related Work
### Existing Methods
Other races (e.g., Latinos) are considerably underrepresented in public face-image collections. Face analytic methods cannot be applied to non-white race groups since the models built on such datasets have inconsistent classification accuracies [24; 25; 26; 27]. These datasets have a race bias problem; thus, the authors created a new face-picture dataset that has photographs from 108,501 people of different races. White, black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino were the seven racial categories the authors identified. The YFCC-100M Flickr dataset was used to capture images, which were then classified by race, gender, and age. Tests were run to gauge generalizability on pre-existing face attribute datasets and brand-new image datasets. When tested on new datasets, the model the authors trained using their data performed significantly better than other models for both male and female participants. In addition, the authors assessed the accuracy of various commercial computer-vision APIs across a range of demographics, including gender, race, and age. Race recognition (RR), which has numerous applications in surveillance systems, image and video interpretation, analysis, and others, is a challenging problem to solve. The use of a deep-learning model to help solve that challenge has been analyzed in [28; 29]. A race recognition framework (RRF) was proposed, which included an information collector (IC), face detection and preprocessing (FD&P), and RR modules. Both independent models were presented here in the study for the RR module. In the first instance, an RR-based CNN model was used (the RR-CNN model). The trained model (RR-VGG) was a fine-tuning model for RR based on the VGG object-recognition model. The dataset is entitled VNFaces and is made up of photographs taken directly from Vietnamese Facebook pages. Their experiments compared the accuracy of the RR-CNN and RR-VGG models based on the suggested framework's performance. On the VNFaces dataset, the RR-VGG model with enhanced input photos had the highest accuracy (88.87%). In comparison, the independent and lightweight model (88.64%) of RR-CNN had the lowest accuracy (88.54%). Extension experiments showed that the authors' models may be applied to other race dataset problems, such as Japanese, Chinese, or Brazilian, with over 90% accuracy; the fine-tuning RR-VGG model attained the best accuracy and was suggested for the majority of situations. Ethnicity plays a fundamental and significant role in biometric recognition because it is a characteristic of human beings. A new approach to ethnicity classification is presented in [8]. Methods for identifying ethnicity by extracting characteristics from facial photos and developing a classifier based on these features are commonly used. Deep CNNs were used instead to extract and classify features simultaneously. The proposed method was tested on three populations: blacks and whites, Chinese and non-Chinese, and Han, Uyghurs, and other non-Chinese people. The proposed method was tested on both public and self-collected databases and found to be effective. Race classification in facial image analysis has been a long-standing problem [28]. To avoid analyzing every area of the face, focusing on the most noticeable ones is critical. The classification of ethnicity and race can be greatly aided by face segmentation, which is an important part of many face analysis tasks. A race-classification technique based on a face segmentation framework was proposed in [13]. A face segmentation model was built using a deep convolutional neural network (DCNN). The DCNN was trained by labeling facial images with seven different classes, which included the nose (skin), skin (hair), eyebrows (eyelid), and the back (mouth). It was employed in the first phase to generate segmentation results. Probability maps (PMs) were constructed for each semantic class using the probabilistic classification approach. Five of the seven most important facial traits for determining a person's race were examined in depth.