23,717 research outputs found

    The Effect of Orthodontic Appliances on the Evaluation of the Professionalism and Esthetics of an Adult Employee

    Get PDF
    This study explored the influence of fixed and removable orthodontic appliances on participants’ ratings of the job performance, intelligence, and attractiveness of an adult female. Ninety-four adult subjects were recruited from the Graduate School of Management at Marquette University. Each subject received an identical employee performance review with an attached photograph of a female employee. The smile of the photo was manipulated to represent one of four conditions: no orthodontic appliance, a metal orthodontic appliance, a ceramic orthodontic appliance, or a clear aligner. Subjects then rated the employee on three continuous Likert scales. Ratings of job performance, intelligence, and attractiveness were not correlated. There were no significant differences between the types of orthodontic appliance for overall ratings of job performance, intelligence, and attractiveness. However, when analyzed by the subject’s gender, there was a significant interaction between gender and type of orthodontic appliance pictured for intelligence ratings. Female respondents rated the photos with the metal appliance with lower intelligence than the photo with the clear aligner while male respondents answered in the opposite manner. Background facial attractiveness may be a better predictor than smile esthetics of the psychosocial ratings of individuals. However, both gender and the presence or absence of an orthodontic appliance can influence assessments of perceived intelligence or similar qualities in the workplace

    Proposing a hybrid approach for emotion classification using audio and video data

    Get PDF
    Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

    Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition

    Full text link
    In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201

    Video surveillance for monitoring driver's fatigue and distraction

    Get PDF
    Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers
    • …
    corecore