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In this study, we hypothesized that the tendency toward an age estimation bias when judging age based on facial images was driven by relative comparison with one's own age, similar to situations of face-to-face communication. Using facial images as stimuli, participants were asked to assess the ages of those in the images in relative terms (younger...
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... to confirm the bias effect in age estimation, the b values for each group (based on gender and age) and condition (neutral vs smiling face) were tested against 0 (baseline) using paired t-tests. As shown in Table 1 and Table 2, for both expression conditions, the bias effects in age estimation were found throughout all groups, with the exception of the middle-age female group. Note that the significance level of the bias effect was marginal in the young-middle age group for the neutral expression condition. ...Similar publications
In this study, we hypothesized that the tendency toward an age estimation bias when judging age based on facial images was driven by relative comparison with one's own age, similar to situations of face-to-face communication. Using facial images as stimuli, participants were asked to assess the ages of those in the images in relative terms (younger...
In this study, we hypothesized that the tendency toward an age estimation bias when judging age based on facial images was driven by relative comparison with one's own age, similar to situations of face-to-face communication. Using facial images as stimuli, participants were asked to assess the ages of those in the images in relative terms (younger...
Most theories of social exchange distinguish between two different types of cooperation, depending on whether or not cooperation occurs conditional upon the partner’s previous behaviors. Here, we used a multinomial processing tree model to distinguish between positive and negative reciprocity and cooperation bias in a sequential Prisoner’s Dilemma...
Citations
Recent research works have been focussing on estimating age from facial images. Age estimation from faces basically involves two sub-processes: extracting features and estimating learning function. Age classification from an input image is the task at hand in this project; age will be classified into 3 categories: 1) Toddler, 2) Teen, 3) Adult. Classifying age automatically from an image has been widely used in our day-to-day lives, particularly in the listed fields: biometrics, surveillance systems, and commercial kiosks. The purpose of this study is to categorize facial images based on their age. Prominently, previously existing research works were performed on contrived and unreal images curated in laboratories. Those images did not correctly portray the distinctions and fluctuations that are evident in real human faces. This paper uses deep convolutional neural networks (CNN) on the available data to overcome the above discussed challenge.
