Development, Reliability, and Validity of the Japanese Version of Ten Item Personality Inventory (TIPI-J)

Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single-and mixed-illuminant scene color correction. 

Auto White-Balance Correction for Mixed-Illuminant Scenes

Humans' ability to discriminate a Duchenne (genuine) smile from non-Duchenne (“presented”) smile attracted scientific interests among various types of smiles. To better understand humans' capability and its mechanisms to discriminate Duchenne and non-Duchenne smiles, we examined people's observational behaviors during Duchenne/non-Duchenne discrimination tasks using an eye-tracker. In so doing, we also collected data on participants' personality traits as some previous studies showed that observational behaviors are strongly influenced by the personality traits of observers. With two Experiment, we found differences in observational behaviors between correct and incorrect discrimination, and that different personality traits were associated with different observational behaviors. However, we did not found any significant relationship between observers' personality traits and judgment accuracies. Thus, although we found differential observational behaviors for correct and incorrect respondents, it seems that these differences were nothing to do with participants' personality traits.

How do the personality traits affect observational behaviors when judging whether smiles are genuine or not

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System

Previous research on personality attribution to faces implicitly assumed that particular features of faces and their configurations lead to particular patterns of personality attribution uniformly. Xu and his colleague raised a concern about the validity of this assumption by showing features of observers also influence impression ratings of faces. In particular they showed that observers’ personalities influence their observational behaviors which in turn influence impression ratings of faces. The present research examined the direct effects of observers’ personalities on impression ratings of face while controlling for observational behaviors. Our results showed that there were some participants with particular personalities tried to look at non-targeted areas of faces even when those areas were masked and not visible to them, indicating robustness of the effect of personality on impression ratings. In addition, we were able to show that there were direct effects of observers’ personalities on impression ratings on faces while controlling for observational behaviors.

Direct effects of personality traits of observers on impression ratings of faces

The vast majority of previous research on facial impressions dealt with how individual features (e.g. shapes of eyes) and/or their configurations affect impression formation on face. These research implicitly assume that the effects are uniformly applicable to many if not all observers. But, we believe the validity of this assumption questionable. For example, a shy person, who is hesitant to look at other individuals eyes, and an outgoing person, who constantly looks at others eyes, may form different impressions to a particular face whose eyes are very characteristic, simply because those two individuals look at different areas of the face. In the present research we hypothesized that some features of observers also affect impression formation. In order to examine our hypothesis, we conducted two experiments. In Experiment 1, we examined how observers personalities and observational behaviors, recorded by an eye-tracking device, affected formed impression. Our data were analyzed with hierarchical Bayesian models. The result showed that both the observers personality and observational behaviors influences formed impressions. In order to distinguish the effects of observers personalities and observational behaviors, we induced participants to look at particular areas of faces in Experiment 2. The results indicated that observers personalities influence observational behaviors, which in turn influence impression formation on face.

Effects of observers' characteristics on impression formation on face

In this paper, we investigated the effects of visual and auditory adaptation on material appearance. The target in this study was metallic perception. First, participants evaluated CG images using sounds and other images. In the experiment, we prepared metallic stimulus under various
 adaptation conditions with different combinations of metal image, non-metal image, metal sound, and non-metal sound stimuli. After these adaptations, the participants answered "metal" or "non-metal" after viewing a displayed reference image. The reference images were generated
 by interpolating metal and non-metal images. Next, we analyzed the results and clarified the effects of visual, auditory, and audiovisual adaptations on the metallic perception. For analyzing results, we used a logistic regression analysis based on Bayesian statistics. From the analysis results,
 we found visual and auditory adaptation effects. On the other hand, we did not find the cross-modal effects of audiovisual adaptation. Finally, we created a model of the linear sum of the visual and audio adaptation effects on metallic material appearance.

Investigating Effects of Visual and Auditory Adaptation on Metallic Material Appearance


 Many previous studies on facial impression inference have focused on the physical features of the face. However, few have considered the effect of the observer on impression inference. Further, many studies have treated observers’ characteristics with regards to facial impression inference as errors or individual differences and thus largely excluded it from the explanatory variables. The present study, on the other hand, focused on the observers’ characteristics and examined whether the observers’ personality traits and observational behaviors influence the impression inferences of the faces. Experiment 1 examined the relationship between observer characteristics (observational behaviors and personality traits) and facial impression inferences. We found that the observers' personality traits strongly influence observational behaviors, but neither of those influences the impression inferences. Using hierarchical Bayesian models, we found a large proportion of faces’ random effect, confirming many previous studies that facial features indeed affect how the faces are seen. The results of Experiment 1 indicate there might have been an interactive effect of observational behaviors and faces’ physical features on impression inferences. To control the potential interactions, we instructed participants to look at particular areas of faces during impression inference tasks in Experiment 2. Although participants generally looked at the areas instructed to look at, we still found a robust relationship between participants' personality traits and observational behaviors. The results show that participants inferred different impressions, even for the same faces, when they were instructed to look at different areas of faces. We also found significant relationships between participants’ personality traits and impression inference ratings, suggesting people with different personality traits would have different impressions for the very same faces.

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Kuangzhe Xu

Papers

Direct effects of personality traits of observers on impression ratings of faces

Effects of observers' characteristics on impression formation on face

Investigating Effects of Visual and Auditory Adaptation on Metallic Material Appearance

How do the personality traits affect observational behaviors when judging whether smiles are genuine or not

How Observer Characteristics Affect How They Infer Personality Impressions of Faces