## 2 Related Work
### Existing Methods
Face photos were then recognized and retrieved using demographic features. The demographic estimates from a state-of-the-art algorithm and the proposed approach were also shown. The MORPH and FERET face datasets showed that the suggested strategy can compete with existing approaches for recognizing face photos across aging differences in recognition accuracy. Many deep-learning (DL) algorithms have recently been developed for diverse applications, and those for face recognition (FR), in particular, have taken a huge leap. Deep FR systems benefit from the hierarchical architecture of DL approaches to develop discriminative face representations. On the other hand, DL approaches considerably enhance the current state-of-the-art FR systems and stimulate a wide range of efficient and diversified real-world applications. FR systems that use several types of DL approaches were examined, and 171 recent contributions were summarized for the study in [18]. Those authors addressed works on DL-based FR systems that deal with various algorithmic and architectural aspects as well as the present and future trends in the field. Afterward, they discussed various activation and loss functions for various DL approaches to better grasp the present state of the art. They also included a summary of the datasets used in FR tasks and problems relating to illumination, expressions, position variations, and occlusion, which were discussed in detail. Their final discussion looked at ways to improve FR duties and future developments. Despite recent advances in this area, ethnicity recognition with deep neural networks has received less attention from the scientific community than other facial soft biometrics such as gender and age. To train CNNs for ethnicity recognition, the authors need a large and representative dataset, which is currently unavailable. In addition, gathering new ethnicity datasets is difficult; people are trained to recognize the basic ethnicity groups using somatic facial features that must be carried out manually. The VGGFace2 Mivia Ethnicity Recognition (VMER) dataset, which contains more than 3,000,000 face pictures annotated with four ethnicity categories--African American, East Asian, Caucasian Latin, and Asian Indian--fills this gap in facial soft biometrics research. To prevent the bias produced by the well-known race effect, the final annotations were obtained using a methodology that requires the judgment of three people belonging to various ethnicities. VGG-16, VGG-Face, ResNet-50, and MobileNet v2 were some of the prominent deep network architectures analyzed in [10]. Last, but not least, those authors conducted a cross-dataset evaluation to show that deep network architectures trained using VMER generalized better across diverse testing datasets than similar models trained on the largest ethnicity dataset available. The ethnicity labels for the VMER dataset and the code used in the studies are available at [[https://mivia.unisa.it](https://mivia.unisa.it)]([https://mivia.unisa.it](https://mivia.unisa.it)) (accessed on 6 June 2022) upon request. One of the most intriguing study areas in computer vision is face recognition. Deep-learning techniques such as the CNN have made strides in recent years. Face recognition has been a huge success for CNNs. Computer face recognition is a method used to identify people's faces in pictures. Several researchers have studied facial recognition. This report summarized researchers' work on facial recognition using a CNN [19], covering studies that have been published during the last five years, including those that were cited. Face recognition using a CNN was tested to see if a renewal had occurred. CNNs, facial recognition, and a description of the database that has been utilized in numerous studies are all included in this article's theoretical foundations. The survey aimed to yield new insights into facial recognition based on CNNs. Face recognition and ethnicity recognition are closely related as they both involve analyzing and identifying characteristics from a person's face.