A stereotype, according to social psychology, is a fixed, overgeneralized view about a class or a specific group of individuals. Face recognition, on the other hand, is a method of recognizing a human face using technology. Biometrics are used in face recognition to extract facial traits from a video or image. Facial recognition is the process of studying a person’s facial characteristics and creating an opinion about them, their mood, whether they are neutral or menacing, and whether they are aggressive or friendly (Kleider-Offutt et al., 2021). The visual pathway is involved in the process; the eye receives the visual information and sends it to the brain.
The visual input, on the other hand, may be same; nonetheless, various brains process and produce distinct perceptions from the same picture. This is due to prejudices in each person’s thinking that affect their understanding of the face. Individuals are oblivious to the existence of stereotypes in their minds. Certain visual traits, such as a large nose or small lips, were shown to be the foundation of stereotypes that were regarded as menacing in studies. Individuals formed impressions of others based on certain face traits rather than ethnicity, gender, or age (Kleider-Offutt et al., 2021). Other elements that strongly impact face recognition abilities include social position, politics, visual content, intentions, and prejudice (Barnett et al., 2020).
More research is needed to understand the complete spectrum of stereotypes and their meaning, as well as the extent of these views and their impacts. The fundamental hypothesis for performing this research is that there is a substantial association between stereotyping and misunderstanding of face characteristics. The goal of the research is to see whether the above hypothesis is correct or if it should be rejected. The literature on facial first impressions has mostly focused on trait dimensions, with little attention paid to how social media categories such as gender may impact initial impressions of faces. Gender categories have been demonstrated to be important in judging behavior in social media research. An examination of whether the gender of a person’s face influences their initial impressions, both negatively and positively. Female counter-stereotyping on the face was seen as more detrimental than male facial stereotyping ( Zingora, Vezzali & Graf, 2020). These findings combine face first impressions research with social category-based evaluation variations.
The cognitive model is the one that is used the most in face recognition on a daily basis. The procedure enables individuals in a given setting to engage with one another (Watkins 2020, October). The building blocks of cognition are concepts. The goal of ideas is to provide labels that may be used to group items together. Concepts are an essential component of one’s daily mental process. They’re mental shortcuts that help you grasp things more quickly and efficiently. Developing face recognition is a crucial component of complicated social structures. People may shape how they connect with one another and comprehend their immediate environment by being able to distinguish identity, sex, mood, age, and race. Even though facial perception is thought to be mostly based on visual input, research have demonstrated that even persons born blind may acquire face perception without vision.
The human face contains a wealth of social information, including race. Regardless of how facial information is processed, race has a significant impact on face recognition accuracy. The own-race bias, also known as the other-race effect or cross-race effect, describes how own-race faces are identified more easily than those of other races. Own-race prejudice has been studied extensively in people of numerous races and cultures, including those of African, Caucasian, and Asian heritage, as well as adults and newborns as young as three months old ( Palum et., 2017). The concept that underlying physical differences in face characteristics across races may make discrimination easier within certain races than others was an early explanation for own-race prejudice. However, there is no evidence to suggest that faces of one race are physically more homogeneous than those of other races, based on either behavioral or anthropometric data.
The contact hypothesis, which appeals to the number of encounters individuals have with own-race faces vs other-race faces, is another prominent definition of own-face prejudice. According to the contact hypothesis, an individual’s degree of interaction with another race is positively related to their ability to recognize faces of that race (Masi et al., 2016, October). To present, the issue of the function of lifetime integrated exposure in regulating own-race prejudice has remained unclear and deserves additional exploration. It’s worth mentioning that the perceptual experience hypothesis hasn’t been thoroughly investigated in multiracial groups. Only a few studies included other races, with white and black populations accounting for almost all of the study on own-race. Among recent decades, a significant amount of research has focused on own-race prejudice in East Asian communities. However, these results may not apply to other racial groupings since the social environment for people from multiracial countries might be more complex and varied than for those from monoracial societies.
A few research on multiracial communities have shown some empirical evidence for the perceptual experience theory, demonstrating that own-race prejudice is decreased in multiracial groups when other-race faces are often seen and identified. Presenting a stereotype-congruent occupational title improves the accuracy with which previously unknown faces are identified. Using the label ‘criminal’ during encoding and testing, for example, promotes recognition for previously new faces that resemble the stereotype criminal. Furthermore, the people and occurrences are unlikely to have been seen before, and hence the eyewitness is stereotypically unfamiliar with them. Encoding conditions have been found to have a bigger impact on successful recognition than retrieval circumstances in the realm of face processing (Neoh et al., 2015).
Face recognition was evaluated by Winograd after he completed one of nine assessments during the first encoding of a face (Schwartz & Yovel 2019). While making physical judgements about people’s features, such as straight hair or a prominent nose, recognition accuracy was lower than when making abstract assessments, such as clever (Rezlescu et al., 2017). There is no direct evidence, however, that forming personality or occupation judgements about a face entails or results in the processing of a higher amount of attributes than making a gender organization.
Barnett, B. O., Brooks, J. A., & Freeman, J. B. (2020). Stereotypes bias face perception via orbitofrontal–fusiform cortical interaction. Social Cognitive and Affective Neuroscience, 16(3), 302–314. https://doi.org/10.1093/scan/nsaa165
Kleider-Offutt, H., Meacham, A. M., Branum-Martin, L., & Capodanno, M. (2021). What’s in a face? The role of facial features in ratings of dominance, threat, and stereotypicality. Cognitive Research: Principles and Implications, 6, 53. https://doi.org/10.1186/s41235- 021-00319-9.
Kleider-Offutt, H., Meacham, A. M., Branum-Martin, L., & Capodanno, M. (2021). What’s in a face? The role of facial features in ratings of dominance, threat, and stereotypicality. Cognitive Research: Principles and Implications, 6(1), 1-14.
Masi, I., Trần, A. T., Hassner, T., Leksut, J. T., & Medioni, G. (2016, October). Do we really need to collect millions of faces for effective face recognition?. In European conference on computer vision (pp. 579-596). Springer, Cham.
Neoh, S. C., Zhang, L., Mistry, K., Hossain, M. A., Lim, C. P., Aslam, N., & Kinghorn, P. (2015). Intelligent facial emotion recognition using a layered encoding cascade optimization model. Applied Soft Computing, 34, 72-93.
Palumbo, R., Adams Jr, R. B., Hess, U., Kleck, R. E., & Zebrowitz, L. (2017). Age and gender differences in facial attractiveness, but not emotion resemblance, contribute to age and gender stereotypes. Frontiers in psychology, 8, 1704.
Rezlescu, C., Susilo, T., Wilmer, J. B., & Caramazza, A. (2017). The inversion, part-whole, and composite effects reflect distinct perceptual mechanisms with varied relationships to face recognition. Journal of Experimental Psychology: Human Perception and Performance, 43(12), 1961.
Schwartz, L., & Yovel, G. (2019). Learning faces as concepts rather than percepts improves face recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(10), 1733.
Watkins, E. A. (2020, October). Took a pic and got declined, vexed and perplexed: facial recognition in algorithmic management. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (pp. 177-182)
Zingora, T., Vezzali, L., & Graf, S. (2020). Stereotypes in the face of reality: Intergroup contact inconsistent with group stereotypes changes attitudes more than stereotype-consistent contact. Group Processes & Intergroup Relations, 1368430220946816.