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<p>Older adults tend to show overall recognition deficits and qualitatively different patterns in the particular expressions that are most difficult to identify (Ruffman et al., 2008). In the current study, 23 younger (18-33 years old) and 23 older (60-80 years old) adults performed a 4AFC (angry, fearful, happy, sad) facial expression categorization task varying orientation (upright/inverted) and stimulus duration (100, 500, 1000 ms). For both groups, happiness was the easiest expression to identify and fear and sadness were the most difficult. Compared to younger adults, older adults were more affected by stimulus orientation, and generally benefit less from increased stimulus duration. For upright faces, there was no age difference in response accuracy but response latency was longer in older subjects. For inverted faces, older adults showed lower accuracy and longer latencies for expressions of anger, fear, and sadness. Recognition of inverted happy faces was spared in older adults for accuracy, but not response latency. These findings could not be explained by impaired detection sensitivity, as no systematic age differences were found for perceived intensity ratings. Finally, the expressions that were most to least difficult to identify was the same in each age group at both orientations. Overall, these results suggest that older individuals process expressive faces in a qualitatively similar way to their younger counterparts, but are less efficient at extracting the diagnostic information.</p> / Master of Science (MSc)

Yes / Face recognition is a very current subject of great interest in the area of visual computing. In the past, numerous face recognition and authentication approaches have been proposed, though the great majority of them use full frontal faces both for training machine learning algorithms and for measuring the recognition rates. In this paper, we discuss some novel experiments to test the performance of machine learning, especially the performance of deep learning, using partial faces as training and recognition cues. Thus, this study sharply differs from the common approaches of using the full face for recognition tasks. In particular, we study the rate of recognition subject to the various parts of the face such as the eyes, mouth, nose and the forehead. In this study, we use a convolutional neural network based architecture along with the pre-trained VGG-Face model to extract features for training. We then use two classifiers namely the cosine similarity and the linear support vector machine to test the recognition rates. We ran our experiments on the Brazilian FEI dataset consisting of 200 subjects. Our results show that the cheek of the face has the lowest recognition rate with 15% while the (top, bottom and right) half and the 3/4 of the face have near 100% recognition rates. / Supported in part by the European Union's Horizon 2020 Programme H2020-MSCA-RISE-2017, under the project PDE-GIR with grant number 778035.

This research study focused on middle school teacher perceptions and experiences regarding how education leaders supported them in the transition to online teaching during a global pandemic. The research question was how did education leaders support middle school teachers to effectively teach English, mathematics, science, and social students online during the pandemic. A survey was sent to 90 middle school teachers who taught English, mathematics, science, or social studies in an online learning modality during the Covid-19 pandemic. Fourteen teachers completed the survey and from the survey respondents 6 teachers participated in a 1:1 virtual interview. A data analysis was process was used to analyze the teacher participants' survey and interview responses regarding how education leaders supported them in the transition to online teaching during the pandemic. The survey results showed 73% of the teachers perceived the principal was supportive in the transition while 80% of teachers perceived the grade-level administrators were supportive. In the interview analysis 100% of the teachers believed leaders were supportive. For professional development received, 60% of teachers surveyed and 83% of the interviewees felt they received enough professional development. Teachers expressed the need for additional professional development in the interviews which was 83% while only 60% of teachers surveyed wanted the additional training. Teachers were asked if leadership effectively supported them in teaching online. Eighty percent of teachers surveyed perceived the support provided by leaders was effective for teaching online. Of the interviewees, 100% felt the principal support was effective and 83% felt the grade-level administrator support was effective. Findings suggest that listening to teachers' issues, concerns, and suggestions, being positive, supportive, assisting in parent communication, monitoring poor student attendance, and providing professional development for online teaching were critical for teachers' effectiveness in the transition to online teaching. The findings of this study provided for a better understanding of how education leaders supported teachers during a global crisis. The information obtained can provide a significant contribution for education leaders and teachers in the development of virtual course curriculum and instructional strategies in the K-12 online learning modality. / Doctor of Education / This study investigated the middle school teachers' perceptions and experiences regarding how education leaders provided support to them in the transition to online teaching during a global pandemic Supporting research on education leadership support, teacher perceptions, online teaching, and Covid-19 is included. The researcher conducted this study to determine how the education leaders supported teachers during the transition to effectively teach online. The research question was how did education leaders support middle school teachers to effectively teach English, mathematics, science, and social students online during the pandemic? A survey was sent to 90 middle school teachers who taught English, mathematics, science, or social studies in an online learning modality during the pandemic. Fourteen teachers completed the survey and from the survey respondents 6 teachers participated in a 1:1 virtual interview. A data analysis was process was used to analyze the teacher participants' survey and interview responses regarding how education leaders supported them in the transition to online teaching during the pandemic. Findings suggest that listening to teachers' issues, concerns and suggestions, being positive and supportive, assisting in parent communication, monitoring poor student attendance, and providing teacher professional development for online teaching were critical for teachers to be effective in the transition to online teaching.

The current study explored coping strategies toward the context of discovering a partner's sexual infidelity. There were two primary goals for the current study. First, the current study examined the gender and national differences in conflict styles toward the context of discovering a partner's sexual infidelity. Second, the current study examined the relationship between face concerns and commitment. There were national and gender differences on self-construal face concern, face-concern and conflict styles in the current study. The current findings also suggested that face concern is moderating the relationship between commitment and conflict styles. The limitation and future directions were discussed as well as the clinical implications in the current study. / Ph. D.

Face recognition is one of the most natural activities performed by the human beings. It has wide range of applications in the areas of Human Computer Interaction, Surveillance, Security etc. Face information of people can be obtained in a non-intrusive manner, without violating privacy. But, robust face recognition which is invariant under varying pose, illumination etc is still a challenging problem. The main aim of this thesis is to explore the usefulness of smile profile of human beings as an extra aid in recognizing people by faces. Smile profile of a person is the sequence of images captured by a camera when the person voluntarily smiles. Using sequence of images instead of a single image will increase the required computational resources significantly. The challenge here is to design a feature extraction technique from a smile sample, which is useful for authentication and is also efficient in terms of storage and computational aspects. There are some experimental evidences which support the claim that facial expressions have some person specific information. But, to the best of our knowledge, systematic study of a particular facial expression for biometrical purposes has not been done so far. The smile profile of human beings, which is captured under some reasonably controlled setup, is used for first time for face recognition purpose. As a first step, we applied two of the recent subspace based face classifiers on the smile samples. We were not able to obtain any conclusive results out of this experiment. Next we extracted features using only the difference vectors obtained from smile samples. The difference vectors depend only on the variations which occur in the corresponding smile profile. Hence any characterization we obtain from such features can be fully attributed to the smiling action. The feature extraction technique we employed is very much similar to PCA. The smile signature that we have obtained is named as Principal Direction of Change(PDC). PDC is a unit vector (in some high dimensional space) which represents the direction in which the major changes occurred during the smile. We obtained a reasonable recognition rate by applying Nearest Neighbor Classifier(NNC) on these features. In addition to that, these features turn out to be less sensitive to the speed of smiling action and minor variations in face detection and head orientation, while capturing the pattern of variations in various regions of face due to smiling action. Using set of experiments on PDC based features we establish that smile has some person specific characteristics. But the recognition rates of PDC based features are less than the recent conventional techniques. Next we have used PDC based features to aid a conventional face classifier. We have used smile signatures to reject some candidate faces. Our experiments show that, using smile signatures, we can reject some of the potential false candidate faces which would have been accepted by the conventional face classifier. Using this smile signature based rejection, the performance of the conventional classifier is improved significantly. This improvement suggests that, the biometric information available in smile profiles does not exist in still images. Hence the usefulness of smile profiles for biometric applications is established through this experimental investigation.