Lab report on facial recognition experiment Introduction According to Atay et al. (2021), facial recognition is a compli

Lab report on facial recognition experiment
Introduction
According to Atay et al. (2021), facial recognition is a complicated process that helps in remembering and identifying the faces of humans. The research was meant to understand the significance of the differences in recognizing human faces of different genders. Rehnman & Herlitz (2007) insists that women are better at recognizing and remembering faces than men. This is related to differences in evolutionary, social, and cognitive factors. Other factors leading to the difference are memory and attention. This study seeks to replicate and improve the findings of previous research on comparing the facial recognition discrepancy between men and women. The hypothesis to be tested is that females have higher accuracy in facial recognition than males.
Methods
Participants
In the study, forty participants were involved to ensure that the results could be generalizable. Twenty of the participants were males, and twenty were females. They were recruited to go online on a voluntary basis and were given the right to privacy and to leave the study whenever they felt uncomfortable.
Materials and procedure
The materials used were a Zoom online platform where people can interact remotely without physically meeting each other. The Zoom platform was used during the presentation of the unfamiliar female and male faces to the participants. The procedure involved participants completing facial recognition tasks after viewing specific faces, which was used to evaluate their abilities to remember the faces from a larger group of images. The accuracy of each participant was noted together based on the total faces identified.
Data analysis and results.
To determine the mean accuracy rates, the data recorded showed the significance of the difference between the faces identified collected and those that were not correct. The standard deviation and mean were calculated, and an independent sample t-test was conducted to determine how each gender performed. The results for the male participants were as follows: 3 6 6 4 6 6 6 5 3 3 6 6 9 5 4 5 7 6 6 7 while for the females were as follows: 7 6 7 8 7 10 8 8 8 10 8 9 9 8 8 9 9 8 8 9.
The standard deviation for the males was 1.62, while the mean was 5.5. In contrast, the standard deviation for the females was 1.17, and the mean was 8.15. An independent t-test was conducted to determine the accuracy scored for the males and females. The results revealed that there was a significant difference between the two groups since the females performed higher than the males.
Discussion
The results support the initial hypothesis that males have inferior facial recognition capabilities compared to males. Therefore, the results support the findings of Rehnman & Herlitz (2007), who also identified that women had superior facial recognition abilities. There are several factors that may affect such results. Firstly, women may be using a holistic processing method where they use a few specific features of the face, not the whole face. This could improve their abilities to differentiate between the faces of the men. Additionally, women may have culturally developed better features to recognize faces due to their caregiving roles and interactions with society members. Finally, the women could have been more attentive in the study, which is why they could have identified faces faster while the men could not have been attentive. Future research could focus on technological tools that could be used for facial recognition other than memory.
Conclusion
In conclusion, facial recognition is a gradual process where people identify and remember faces. This study has recognized that women have superior facial recognition capabilities compared to men. This could be attributed to attentiveness and caregiving. There is a need for additional research to identify how computing software, especially in smartphones, could be used in facial recognition since people are prone to forgetting.

References
Atay, M., Gipson, H., Gwyn, T., & Roy, K. (2021, December). Evaluation of gender bias in facial recognition with traditional machine learning algorithms. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-7). IEEE.
Rehnman, J., & Herlitz, A. (2007). Women remember more faces than men do. Acta Psychologica, 124(3), 344-355.