基于人脸的性别识别和年龄估计广泛存在于众多现实应用之中（如受限访问控制和商业推荐系统等），近年来已渐受关注。但相比单一的性别识别或年龄估计，对两者同时进行的研究则相对远少得多,原因大致可归咎于1)近年才兴起;2)性别与年龄不同语义导致的异质性和年龄的离散有序性等带来的挑战等。尽管研究正处在进展中，但现有工作对1)两者语义的异质性和年龄的有序性;2)相对更可靠的相对年龄信息利用和3)性别和年龄共同影响因素的探索等,未作全面考虑。本项目旨在先前工作基础上,通过综合上述诸项，尝试建立一个统一而全面的学习框架，达到在单一优化目标下克服现有工作遗留的关键问题，弥补不足并提升此方面的研究水平。为此侧重研究1)既能保留现有工作优点又能克服上述诸遗留问题的单一目标建模及其优化;2)实验验证和比较;3)至其他相关学习场景的拓展等。可望所获成果不仅能为该特定问题同时也能为更一般的类似学习问题的解决提供借鉴。

Face-based gender recognition and age estimation exists in many real-world applications (such as access-restricted control and bussiness recommendation systems,etc.) and has recently gradually received much attention, however, compared to individual gender recognition or age estimation research, joint research on both aspects is relatively much less,the reason can roughly be due to 1) just recent emergence;2)challenge from both semantic heterogeneity hidden between gender and age,and discrete ordinality behind age.Though such research is currently ongoing,existing works have not comprehensively considered the following keypoints: 1) the heterogeneity between gender and age,and the ordinality of age itself;2)utilization of more reliable information from relative ages and 3) exploration for common impact factors from both gender and age on final performance, and so on. Based on our previous work,the proposal aims to establish a unified learning framework via jointly taking the above-mentioned yet missed points into account to overcome the crucial problems left by existing works under a single optimization objective,and promote the research. To the end, we focus on 1)modeling and optimizing a single objective which can both retain merits of existing works and overcome their left essential problems;2)sufficient experimental verification and comparisons and 3)extensions to other related learning scenarios etc.Our final achievements can be diresable to provide references for solving both this specific problem and at the same time,more general and similar machine learning problems.