Gender Differences in the Perceptions of Genuine and Simulated Laughter and Amused Facial Expressions

This article addresses gender differences in laughter and smiling from an evolutionary perspective. Laughter and smiling can be responses to successful display behavior or signals of affiliation amongst conversational partners—differing social and evolutionary agendas mean there are different motivations when interpreting these signals. Two experiments assess perceptions of genuine
and simulated male and female laughter and amusement social signals. Results show male simulation can always be distinguished. Female simulation is more complicated as males seem to distinguish cues of simulation yet judge simulated signals to be genuine. Females judge other female’s genuine signals to have higher levels of simulation. Results highlight the importance of laughter and smiling in human interactions, use of dynamic stimuli, and using multiple methodologies to assess perception.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

Facial cleanliness and risk of trachoma in families

Trachoma is the leading infectious cause of blindness worldwide, and epidemiologic studies of factors that may increase the transmission of ocular Chlamydia trachomatis are needed. In two villages in a hyperendemic area of Central Tanzania, 472 (90%) of 527 preschool-aged children were examined for specific signs of unclean faces and presence of trachoma. The odds of trachoma were 70% higher in children with flies and nasal discharge on their faces. Other facial signs were not important. In large families, the odds of trachoma increased 4.8-fold if a sibling had trachoma and 6.8-fold if a sibling had trachoma and an unclean face. Health education strategies aimed at improving face washing need to target cleaning nasal discharge and keeping flies off children's faces.