Face masks degrade our ability to remember face-name associations more than predicted by judgments of learning

In response to the COVID-19 pandemic, face masks became required attire. Face masks obstruct the bottom portion of faces, restricting face processing. The present study examined the influence face masks have on memory predictions and memory performance for new face-name associations. Participants studied face-name pairs presented for 8 s (Experiment 1) or 10 s (Experiment 2). Half of the face-name pairs included a face mask obstructing the nose and mouth of the pictured face, counterbalanced across participants. Participants provided item-by-item judgements of learning (JOLs) and completed subsequent cued recall and associative recognition memory tests. Both experiments demonstrated that face masks impaired memory for newly-learned names, however, the magnitude of the mask impact was under-predicted by JOLs. The presence of a face mask negatively influenced memory performance to a greater degree than participants' JOLs predicted. Results have implications for name learning during pandemics, as well as in settings where face masks are common (e.g., medical field).

Face processing and early event-related potentials: replications and novel findings

This research explores early Event-Related Potentials (ERPs) sensitivity to facial stimuli, investigating various facial features aimed to unveil underlying neural mechanisms. Two experiments, each involving 15 undergraduate students, utilized a multidimensional stimulus set incorporating race, gender, age, emotional expression, face masks, and stimulus orientation. Findings highlight significant modulations in N170 and P200 amplitudes and latencies for specific attributes, replicating prior research and revealing novel insights. Notably, age-related facial feature variations, facial inversion, and the presence of face masks significantly impact neural responses. Several speculative explanations are proposed to elucidate these results: First, the findings lend support to the idea that the increased N170 amplitude observed with facial inversion is closely tied to the activation of object-sensitive neurons. This is further bolstered by a similar amplitude increase noted when masks (effective objects) are added to faces. Second, the absence of an additional amplitude increase, when inverting face images with face masks suggests that neural populations may have reached a saturation point, limiting further enhancement. Third, the study reveals that the latency deficit in N170 induced by facial inversion is even more pronounced in the subsequent ERP component, the P200, indicating that face inversion may impact multiple stages of face processing. Lastly, the significant increase in P200 amplitude, typically associated with face typicality, for masked faces in this study aligns with previous research that demonstrated elevated P200 amplitudes for scrambled faces. This suggests that obscured faces may be processed as typical, potentially representing a default state in face processing.

Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.

Face masks are less effective than sunglasses in masking face identity

The effect of covering faces on face identification is recently garnering interest amid the COVID-19 pandemic. Here, we investigated how face identification performance was affected by two types of face disguise: sunglasses and face masks. Observers studied a series of faces; then judged whether a series of test faces, comprising studied and novel faces, had been studied before or not. Face stimuli were presented either without coverings (full faces), wearing sunglasses covering the upper region (eyes, eyebrows), or wearing surgical masks covering the lower region (nose, mouth, chin). We found that sunglasses led to larger reductions in sensitivity (d') to face identity than face masks did, while both disguises increased the tendency to report faces as studied before, a bias that was absent for full faces. In addition, faces disguised during either study or test only (i.e. study disguised faces, test with full faces; and vice versa) led to further reductions in sensitivity from both studying and testing with disguised faces, suggesting that congruence between study and test is crucial for memory retrieval. These findings implied that the upper region of the face, including the eye-region features, is more diagnostic for holistic face-identity processing than the lower face region.

Biases in human perception of facial age are present and more exaggerated in current AI technology

Our estimates of a person's age from their facial appearance suffer from several well-known biases and inaccuracies. Typically, for example, we tend to overestimate the age of smiling faces compared to those with a neutral expression, and the accuracy of our estimates decreases for older faces. The growing interest in age estimation using artificial intelligence (AI) technology raises the question of how AI compares to human performance and whether it suffers from the same biases. Here, we compared human performance with the performance of a large sample of the most prominent AI technology available today. The results showed that AI is even less accurate and more biased than human observers when judging a person's age-even though the overall pattern of errors and biases is similar. Thus, AI overestimated the age of smiling faces even more than human observers did. In addition, AI showed a sharper decrease in accuracy for faces of older adults compared to faces of younger age groups, for smiling compared to neutral faces, and for female compared to male faces. These results suggest that our estimates of age from faces are largely driven by particular visual cues, rather than high-level preconceptions. Moreover, the pattern of errors and biases we observed could provide some insights for the design of more effective AI technology for age estimation from faces.

Mask-wearing selectivity alters observers’ face perception

Face masks became prevalent across the globe as an efficient tool to stop the spread of COVID-19. A host of studies already demonstrated that masks lead to changes in facial identification and emotional expression processing. These changes were documented across ages and were consistent even with the increased exposure to masked faces. Notably, mask-wearing also changes the state of the observers in regard to their own bodies and other agents. Previous research has already demonstrated a plausible association between observers’ states and their perceptual behaviors. Thus, an outstanding question is whether mask-wearing would alter face recognition abilities. To address this question, we conducted a set of experiments in which participants were asked to recognize non-masked faces (Experiment 1), masked faces (Experiment 2) and novel objects (Experiment 3) while they were either masked or unmasked. Mask wearing hindered face perception abilities but did not modulate object recognition ability. Finally, we demonstrated that the decrement in face perception ability relied on wearing the mask on distinctive facial features (Experiment 4). Together, these findings reveal a novel effect of mask-wearing on face recognition. We discuss these results considering the plausible effect of somatosensory stimulation on visual processing as well as the effect of involuntary perspective taking.