Face age estimation is a type of study in computer vision and pattern recognition. Designing an age estimation or classification model requires data as training samples for the machine to learn. Deep learning method has improved estimation accuracy and the number of deep learning age estimation models developed. Furthermore, numerous datasets availability is making the method an increasingly attractive approach. However, face age databases mostly have limited ethnic subjects, only one or two ethnicities and may result in ethnic bias during age estimation, thus impeding progress in understanding face age estimation. This paper reviewed available face age databases, deep learning age estimation models, and discussed issues related to ethnicity when estimating age. The review revealed changes in deep learning architectural designs from 2015 to 2020, frequently used face databases, and the number of different ethnicities considered. Although model performance has improved, the widespread use of specific few multi-races databases, such as the MORPH and FG-NET databases, suggests that most age estimation studies are biased against non-Caucasians/non-white subjects. Two primary reasons for face age research's failure to further discover and understand ethnic traits effects on a person's facial aging process: lack of multi-race databases and ethnic traits exclusion. Additionally, this study presented a framework for accounting ethnic in face age estimation research and several suggestions on collecting and expanding multi-race databases. The given framework and suggestions are also applicable for other secondary factors (e.g. gender) that affect face age progression and may help further improve future face age estimation research.
Active learning allows students to control their learning style by relating it to their actual experiences. This learning model can increase student motivation and engagement in an interactive environment. However, mentoring has not been actively implemented even though mentoring is one of the important agendas for the next 20 years and in line with education 4.0, which encourages students towards active learning. The study's findings found that the gamification approach is an effective method to increase the level of involvement and motivation of students in active learning. However, gamification becomes less effective when in-depth learning is carried out, and the learning process cannot provide satisfaction to students with high levels of confidence. This group only participates for fun but does not want to socialize with other students because the core of gamification is competitive-oriented. Therefore, the objective of this study is to produce a mentoring-style gamification strategy that has collaborative and social mechanics in a soft skill learning environment to increase the participation level of less sociable students. The hypothesis of this study is to introduce learning as a more motivating experience for all levels, not just outstanding students. Student involvement in the play experience can be translated into an educational context to facilitate learning while influencing personality and behavior, including communication skills, problem-solving, self-motivation, decision-making, and time management. The methodology of this study is through exploratory research, namely, observation, activity development, and effectiveness testing to relate students' experiences in authentic learning situations. The result from this study is a mentoring-style gamification strategy that is a guide for mentors to carry out active learning mentoring activities that involve soft skills with students.
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