## 2 Related Work
### Existing Methods
To test the accuracy of face gender classification algorithms, the National Institute of Standards and Technology (NIST) enlisted the help of five commercial companies and one institution, utilizing a combined corpus of close to one million facial pictures from visas and mugshots [24]. The testing approach adopted by NIST simulated operational reality, where software was shipped and used \"as-is\" without the need for further algorithmic training, which was the goal of the methodology. From a large dataset of photographs taken under controlled lighting, position, and facial expression settings, the core gender categorization accuracy was evaluated by gender, age group, and ethnicity. Comparing results from the limited dataset with those from commonly benchmarked \"in the wild\" (i.e., unrestrained) datasets was an important part of the research. Assessments were made on sketch classification performance and gender verification accuracy based on how many images were taken from each individual. Demographic variables such as categorization, age, ethnicity, and gender have a significant impact on the appearance of the human face, with each category further subdivided into classes such as black and white, male and female, young (18-30), middle age (30-50), and old (50-70). Most students look more like their peers in their age group than they do those in other age groups. Subjects from a wide range of ages, ethnicities, and genders were analyzed to see how the accurate facial verification was [5]. To that end, the authors employed a CNN for feature extraction and demonstrated that their approach outperformed a commercial face recognition engine in terms of performance for specific demographics. Women, young people between the ages of 18 and 30, and African Americans all had inferior biometric verification performance than other demographic groups. Using this strategy, the authors then tested the accuracy of face verification across multiple demographic groups. As a result of their findings, they offered recommendations on how to improve face verification for people of different ethnicities [24]. Unsupervised fair-score normalization was proposed to lessen the impact of bias on face recognition and lead to a large overall performance gain. The author's theory was based on treating \"similar\" people legally by implementing a normalization strategy [25; 26]. Three publicly available datasets were used in experiments conducted under controlled and natural conditions. When gender was considered, the results showed that the author's method eliminated demographic biases by 82.7%. Furthermore, it consistently reduced the bias compared with previous efforts. The total performance was improved by up to 53.2% with a false match rate of 103 and by up to 82.9% with a false match rate of 105, which contrasted with earlier works. Furthermore, the method is not restricted to face biometrics and can be easily integrated into existing recognition systems. Many real-world applications, such as human-computer interaction (HCI), demography-based classification, biometric-based identification, security, and defense, to mention a few, use ethnicity as an important demographic trait of human beings. A person's ethnicity can be deduced from face photographs using a new method presented in [2]. The proposed method employed an SVM with a linear kernel as a classifier, which employed a pre-trained CNN. In contrast to prior research, which used handcrafted features such as Local Binary Pattern (LBP) and Gabor, this technique leveraged translationally invariant hierarchical characteristics learned by the network. To support the authors' claim that their method can handle a wide range of expressions and lighting circumstances, they conducted extensive trials on ten facial databases. There were three classes of ethnicity: Asian, African American, and Caucasian. The average accuracy of classification was 98.28%, 99.66%, and 99.05%, respectively, across all datasets.