Psychology Essay: A Critical Discussion On Facial Recognition

Question

Task:
You’ll already have read and discussed the Liu et al. (2010) article [fMRI], Eimer (2000) article [ERPs] and Pitcher et al. (2007) article [TMS] articles during the tutorials. In your psychology essay, you should discuss evidence from TMS, EEG and fMRI studies (using these articles as a starting point for further reading) to describe current knowledge regarding the neural basis of face processing, considering these in the context of theoretical models of face processing. Your essay should contrast in detail the relative strengths and weaknesses of each technique to evaluate their relative contributions to current understanding of face processing, while also considering how techniques can complement each other to provide converging evidence.

Answer

Background Of Psychology Essay
Not only facial features but also facial expression and the configurations of the feature helps in recognizing the face as a whole. This has been stated by the Appearance-based or Holistic theory of face recognition, which includes both linear and non-linear analysis. Capturing facial variations by constructing a human face model provides a compact representation of the facial image. This construction is aimed at the Model-based method of facial recognition. There are two processes of face processing, namely Biology and Cognitive Development. Face recognition has obtained considerable attention as it contributes to numerous applications. It is undeniably a multifaceted and dynamic process. Face recognition is an extremely challenging task. Several research articles, papers, and reviews have been featured in the study of facial recognition Parmar et al., (2014). There are several techniques to study the behavior imparted by the human face. Face recognition and mapping studies include Transcranial magnetic stimulation, Electroencephalogram, and Functional magnetic resonance imaging. TMS is a non-invasive form that is operated fully outside the body. EEG helps in detecting disorders by monitoring the electric sensitivity of the brain. fMRI is applied to reveal the disruption in normal brain function. Face recognition has obtained considerable attention as it contributes to numerous applications. It is undeniably a multifaceted and dynamic process Peng et al., (2020).

fMRI
As mentioned in the article ("What Is fMRI? Uses, How It Works, Duration, and What to Expect", 2022), fRMI is an effective technique to capture and measure several activities of the brain. It reveals the part of the brain that remains active during any activity. There are some of the major advantages provided by the fMRI technique. Radiations such as X-rays, positron emission tomography or PET, computed tomography or CT are not used by the fMRI technique. The probability of virtual risks is almost nil if done correctly. Evaluation of brain function occurs safely. It is a non-invasive and extremely effective method of face recognition. It is easy to carry out the fMRI method. Very high-resolution images are shaped by this technique. fMRI is way more unbiassed when compared to the outdated survey approaches of psychological evaluation.

Despite such advantages, there also exist some major limitations. The expense required for this technique is extremely high. A clear image can only be captured by fMRI if the person who is being scanned stays fully motionless. The method of work is yet not exactly clear to the researchers. fMRI is an indirect measure of neural activity as it measures Blood Oxygen Level Dependent (BOLD) contrast and not direct neural activity. Rather than focusing on the activities of individual nerve cells, fMRI can only emphasize the blood flow, which is believed to be the most valid complaint. Glover mentioned that the results concluded by this study are hard to interpret. The resolution of this method is a major drawback as compared to others Glover et al., (2011).

As mentioned in the article ("EEG vs. MRI vs. fMRI - What are the Differences?", 2022), in the study of facial recognition, it is possible to achieve a spatial resolution of less than a millimeter at 7 T. But as per, as this technique is considered, 3 T fMRI experiments are operated with around 2-3 millimeters of spatial resolution. The temporal resolution of the fMRI technique has been recorded as 1-4 seconds. On the other hand, other techniques such as EEG experiences a temporal resolution of 1-10 milliseconds. Hence, it is clear that the temporal resolution of fMRI is worse than other existing techniques of facial recognition.

There are several applications and uses of the fMRI technique of facial recognition as mentioned by Jiang. Studying the functions of the brain is crucial before any sort of surgery. If lesions are created before birth, then there remains a high chance of abnormality and misplacement of mental processes. fMRI helps in detecting the exact location of the function. The hemisphere of the human brain representing language varies from person-to-person Jiang et al., (2006). Though mostly represented by the left, but may also be represented by the right. Before proceeding with surgeries, it is vital to know the part of the brain responsible for the representation of language. fMRI is effective in such cases. If the surgery is to be done on the lobe of the brain involved with the representation of the language, then it must be stopped with immediate effect. fMRI is used in several non-surgical applications. It provides insights into neurologic disorders. Sedated children and infants are also benefitted from the help of fMRI. Migraine and epilepsy can be assessed with the help of fMRI. This technique can also prevent strokes in children.

Carter has proved that studies must be strongly powered by statistics. Statistically unpowered studies might lead to drastic results. There is a huge numeral of dependent variables in fMRI, whereas the number of observations is comparatively less. There is an extreme need to address several corrections. Inferences concluded from functional magnetic resonance imaging, or fMRI studies, are reliant on the numerical criteria required to describe diverse brain areas under the experimental operation Carter et al., (2008).

There are several sequential stages in the holistic method of face processing. Researches clearly show that the fMRI technique of facial recognition perfectly relates to the Appearance-based or Holistic theory and Model-based theory of face recognition. The combination of computational modeling that is the combination of fMRI and behavioral techniques, it is possible to find out the performance of human face discrimination and activation of FFA.

EEG
EEG helps to evaluate the neuropsychological changes which are involved in face recognition. The older one has faced various neuropsychological disorders, and EEG is beneficial to identify the severity of the symptoms. Electroencephalogram (EEG) is associated with detecting the activity of the brain by using a small metal disc on the scalp. According to Murashko, the major advantage of this process is to detect the brain tumor, brain damage from a head injury, causes of various "brain dysfunction, inflammation in brain stroke, and sleeping disorders". Facial expression has played a major role in social life. The facial expression is involved in six categories such as happiness, sadness, anger, disgust, fear, and surprise, and this was proved by Shmukler in his studies. There are several processes such as "single-cell recordings, functional brain imaging and event-related potentials (ERPs)" that are beneficial to evaluating facial cell expression and understanding emotional behavior and perception. EEG can evaluate the activity of the brain and relate it to emotional events Murashko & Shmukler, (2019).

EEG is a technically advanced process that integrates is with a wearable sensor that can use to track the first psychological responses of the face. It can help to 2gether biometric measurement of the human body. As a result, the use number of data gathered by using EEG is associated with knowledge of human mood and emotion. In this process, there are several other things also involved, such as an eye-tracking system and a biometrics signal monitoring system to evaluate the facial expression of the individual. Eye tracking, biometrics, and EEG signals can use to study the simple and complex emotions of human beings Ghosh et al., (2018). Devices are selected to collect information regarding the psychology of facial expression. Complex emotion is always associated with empathy, and EEG is also involved in measuring the impact of the human face. Ghosh and many other scientists implied that in this process, the participants are divided into two groups, and data are collected accordingly. The electrodermal activity data indicated that self-regulation is not done properly. There are two different machines has been used for EEG activity to determine the self-regulation and emotions which affect the face. The evaluation of combined data can help to identify facial reactions and emotions associated with it.

Emotion is important for the physiological, biological, and mental stress of the human body. In the case of the sides of the square case of the size of the pointer, emotion is involved in several factors such as subjective experience, behavior, Real Experience, and psychological responses. The facial expression of people expresses the basic emotions of the human Konar et al., (2018). The primary emotions associated with pleasure or dominance have also been expressed in the facial reaction. There is a two-dimensional model that can be used for this purpose.

The complex data is the major disadvantage of EEG procedures. In this procedure, most of the time, professionals are unable to manage the complete form of data and facial recognition, which hurts the diagnosis.

TMS
The facial expression expresses a wide range of social information, emotional state, and attention to the identity of someone. Sliwinska referred in his article that Tran's cranial magnetic stimulation (TMS) is an effective process to evaluate the space network. This process can help to disrupt the Brain areas, which are the effect of facial identity, facial expression, head direction, and visual integration. TMS has played a major role to process the different brain areas which are directly related to facial expression.

TMS is a neurologically normal experiment that can help to identify the expression on the face of an individual. This Framework is beneficial to disrupt the face-selective region and identify a different aspect of face processing. Pitcher mentioned that this Framework is collaborated with FMRI to identify the disruption of the specific facial region and selective region. This model is useful to understand someone's identity based on their facial expression and cognitive process, which are associated with a different part of the brain. The resolution of CMS is based on the one to two semi-coils involved in the face net worth to study the reading directly. The neural activity can be measured by using EF MRI in the selective areas of the face Sliwinska & Pitcher, (2018). Then the effect of stimulation is measured in the FFA area.

TMS and fMRI have been combined and used to evaluate face network connectivity. There are some other models has faced some difficulties with creating multiple face pathways in the visual cortex. The Anatomical study has expressed the connection between the cortex and motion selective area. Based on the result, there are two hypotheses and been concluded to purchase visual cortex into the face network and TBS. TVS has played an effective role to reduce the neural responses in your face. Currently, an "identical approach" was wont to study what result "TBS delivered over the Rpsts" would have the useful property of the extended face process network (Janowska et al. 2021). Patients were scanned "exploitation resting-state fMRI (rs-fMRI)" Before and after, TBS was delivered over the posts or the correct motor cortex. TBS Delivered over the rpsts caused a network-wide reduction in resting-state connectivity across the extended face-processing network. This decrease in connectivity was determined. Not solely between the posts and alternative face-selective areas, but additionally between non-stimulated face-selective areas on the ventral and "medial brain surfaces (e.g. Between Right cigar-shaped face space and also the bilateral amygdalae)" (Janowska et al. 2021). This demonstrates that TBS Delivered over one node during a brain network reduces the useful property of nine between the distributed nodes of that extended network. Crucially, this disruption was Determined "between remote nodes that had not been stimulated".

Conclusion
The face is perhaps the easiest way that makes every individual unique and helps others to distinguish one individual from another. Though there are several drawbacks and limitations of the mentioned techniques in this study, yet, there are various advantages that might be applied for further study. Face recognition is inevitably a highly emerging technology that is believed to have a high demand in the future world. In the last twenty years, the technology of face recognition has come a long way. It can be concluded from the article ("The contribution of neuroimaging (fMRI) to understand of face processing", 2022), that fRMI has contributed to innovative practical findings by transferring the neural level of face processing. New ideas have been opened up in neural representation sections. EEG has shown its valuable contribution to the diagnosis of dementia. This technique also exposed several patterns of alterations when the vascular of the human brain was compared to the progressive damage. As mentioned by Lozeron, in the understanding of pathophysiology and the treatment of dystonia, TMS has revealed its unique contribution Lozeron et al., (2016). The knowledge gap of facial recognition still exists since both old and new approaches to face recognition focus on pairs having a considerable age gap. This results in degradation of performance and must be addressed properly and can be addressed by comprehending the interactive nature of face recognition. Jungsoo Lee mentioned that reducing the similarity existing among child images of different identities is vital for addressing the knowledge gaps Jungsoo Lee et al., (2021).

References
Carter, C. S., Heckers, S., Nichols, T., Pine, D. S., & Strother, S. (2008). Optimizing the design and analysis of clinical, functional magnetic resonance imaging research studies. Biological psychiatry, 64(10), 842-849.
https://www.sciencedirect.com/science/article/abs/pii/S0006322308007816

Ghosh, L., Ghosh, S., Konar, A., Rakshit, P. and Nagar, A.K., (2018), November. Decoding of EEG signals using deep long short-term memory network in face recognition task. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 477-483). IEEE.

Glover, G. H. (2011). Overview of functional magnetic resonance imaging. Neurosurgery Clinics, 22(2), 133-139.
https://www.neurosurgery.theclinics.com/article/S1042-3680(10)00112-9/fulltext
https://www.ijais.org/archives/volume11/number4/barnouti-2016-ijais-451597.pdf

Janowska, A., Balugas, B., Pardillo, M., Mistretta, V., Chavarria, K., Brenya, J., Shelansky, T., Martinez, V., Pagano, K., Ahmad, N. and Zorns, S., (2021). The Neurological Asymmetry of Self-Face Recognition. Symmetry, 13(7), p.1135.

Jiang, X., Rosen, E., Zeffiro, T., VanMeter, J., Blanz, V., & Riesenhuber, M. (2006). Evaluation of a shape-based model of human face discrimination using fMRI and behavioral techniques. Neuron, 50(1), 159-172.
https://www.sciencedirect.com/science/article/pii/S0896627306002054

Li, L., Mu, X., Li, S., & Peng, H. (2020). A review of face recognition technology. IEEE Access, 8, 139110-139120. https://ieeexplore.ieee.org/abstract/document/9145558

(2022). Retrieved 29 March 2022, from https://www.ijais.org/archives/volume11/number4/barnouti-2016-ijais-451597.pdf

What Is fMRI? Uses, How It Works, Duration, and What to Expect. (2022). Retrieved 30 March 2022, from https://psychcentral.com/lib/what-is-functional-magnetic-resonance-imaging-fmri#definition

Murashko, A.A. and Shmukler, A., (2019). EEG correlates of face recognition in patients with schizophrenia spectrum disorders: A systematic review. Psychology essay Clinical Neurophysiology, 130(6), pp.986-996.

Parmar, D. N., & Mehta, B. B. (2014). Face recognition methods & applications. arXiv preprint arXiv:1403.0485.
https://www.researchgate.net/profile/Sasan-Karamizadeh-2/publication/262725509_An_Overview_of_Holistic_Face_Recognition/links/55917dd808ae47a34910a94c/An-Overview-of-Holistic-Face-Recognition.pdf

Sliwinska, M.W. and Pitcher, D., (2018). TMS demonstrates that both right and left superior temporal sulci are important for facial expression recognition. NeuroImage, 183, pp.394-400.

The contribution of neuroimaging (fMRI) to understand of face processing. (2022). Retrieved 30 March 2022, from https://studygroom.com/the-contribution-of-neuroimaging-fmri-to-understand-of-face-processing/

Lozeron, P., Poujois, A., Richard, A., Masmoudi, S., Meppiel, E., Woimant, F., & Kubis, N. (2016). Contribution of TMS and rTMS in the understanding of the pathophysiology and in the treatment of dystonia. Frontiers in neural circuits, 10, 90.https://www.frontiersin.org/articles/10.3389/fncir.2016.00090/full
https://deepai.org/publication/improving-face-recognition-with-large-age-gaps-by-learning-to-distinguish-children